Motif-based analysis of effective connectivity in brain networks

J. Meier; M. Märtens; A. Hillebrand; P. Tewarie; P. Van Mieghem

doi:10.1007/978-3-319-50901-3_54

Motif-based analysis of effective connectivity in brain networks

J. Meier^*, M. Märtens, A. Hillebrand, P. Tewarie, P. Van Mieghem

^*Corresponding author for this work

Network Architectures and Services

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

1 Citation (Scopus)

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.

Original language	English
Title of host publication	Complex Networks and their Applications V
Subtitle of host publication	Proceedings of the 5th International Workshop on Complex Networks and their Applications (COMPLEX NETWORKS 2016)
Editors	H. Cherifi, S. Gaito, W. Quattrociocchi, A. Sala
Place of Publication	Cham
Publisher	Springer
Pages	685-696
Number of pages	12
ISBN (Electronic)	978-3-319-50901-3
DOIs	https://doi.org/10.1007/978-3-319-50901-3_54
Publication status	Published - 2017
Event	5th International Workshop on Complex Networks and their Applications: 5th International Workshop on Complex Networks and their Applications - Milan, Italy Duration: 30 Nov 2016 → 2 Dec 2016 Conference number: 5 http://complexnetworks.org/index2016.html http://complexnetworks.org/index2016.html

Publication series

Name	Studies in Computational Intelligence
Volume	693
ISSN (Print)	1860-949X

Conference

Conference	5th International Workshop on Complex Networks and their Applications
Country/Territory	Italy
City	Milan
Period	30/11/16 → 2/12/16
Internet address	http://complexnetworks.org/index2016.html http://complexnetworks.org/index2016.html

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10.1007/978-3-319-50901-3_54

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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 H. Cherifi, S. Gaito, W. Quattrociocchi, & A. Sala (Eds.), Complex Networks and their Applications V: Proceedings of the 5th International Workshop on Complex Networks and their Applications (COMPLEX NETWORKS 2016) (pp. 685-696). (Studies in Computational Intelligence; Vol. 693). Springer. https://doi.org/10.1007/978-3-319-50901-3_54

Meier, J. ; Märtens, M. ; Hillebrand, A. et al. / Motif-based analysis of effective connectivity in brain networks. Complex Networks and their Applications V: Proceedings of the 5th International Workshop on Complex Networks and their Applications (COMPLEX NETWORKS 2016). editor / H. Cherifi ; S. Gaito ; W. Quattrociocchi ; A. Sala. Cham : Springer, 2017. pp. 685-696 (Studies in Computational Intelligence).

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title = "Motif-based analysis of effective connectivity in brain networks",

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.",

author = "J. Meier and M. M{\"a}rtens and A. Hillebrand and P. Tewarie and {Van Mieghem}, P.",

year = "2017",

doi = "10.1007/978-3-319-50901-3_54",

language = "English",

series = "Studies in Computational Intelligence",

publisher = "Springer",

pages = "685--696",

editor = "H. Cherifi and S. Gaito and W. Quattrociocchi and A. Sala",

booktitle = "Complex Networks and their Applications V",

note = "5th International Workshop on Complex Networks and their Applications : 5th International Workshop on Complex Networks and their Applications ; Conference date: 30-11-2016 Through 02-12-2016",

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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 H Cherifi, S Gaito, W Quattrociocchi & A Sala (eds), Complex Networks and their Applications V: Proceedings of the 5th International Workshop on Complex Networks and their Applications (COMPLEX NETWORKS 2016). Studies in Computational Intelligence, vol. 693, Springer, Cham, pp. 685-696, 5th International Workshop on Complex Networks and their Applications, Milan, Italy, 30/11/16. https://doi.org/10.1007/978-3-319-50901-3_54

Motif-based analysis of effective connectivity in brain networks. / Meier, J.; Märtens, M.; Hillebrand, A. et al.
Complex Networks and their Applications V: Proceedings of the 5th International Workshop on Complex Networks and their Applications (COMPLEX NETWORKS 2016). ed. / H. Cherifi; S. Gaito; W. Quattrociocchi; A. Sala. Cham: Springer, 2017. p. 685-696 (Studies in Computational Intelligence; Vol. 693).

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

TY - GEN

T1 - Motif-based analysis of effective connectivity in brain networks

AU - Meier, J.

AU - Märtens, M.

AU - Hillebrand, A.

AU - Tewarie, P.

AU - Van Mieghem, P.

N1 - Conference code: 5

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N2 - 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.

AB - 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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BT - Complex Networks and their Applications V

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Meier J, Märtens M, Hillebrand A, Tewarie P, Van Mieghem P. Motif-based analysis of effective connectivity in brain networks. In Cherifi H, Gaito S, Quattrociocchi W, Sala A, editors, Complex Networks and their Applications V: Proceedings of the 5th International Workshop on Complex Networks and their Applications (COMPLEX NETWORKS 2016). Cham: Springer. 2017. p. 685-696. (Studies in Computational Intelligence). doi: 10.1007/978-3-319-50901-3_54

Motif-based analysis of effective connectivity in brain networks

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