nach oben

Erschienen in:

2018 | OriginalPaper | Buchkapitel

Construction of Sparse Weighted Directed Network (SWDN) from the Multivariate Time-Series

verfasst von : Rahilsadat Hosseini, Feng Liu, Shouyi Wang

Erschienen in: Brain Informatics

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

There are many studies focusing on network detection in multivariate (MV) time-series data. A great deal of focus have been on estimation of brain networks using functional Magnetic Resonance Imaging (fMRI), functional Near-Infrared Spectroscopy (fNIRS) and electroencephalogram (EEG). We present a sparse weighted directed network (SWDN) estimation approach which can detect the underlying minimum spanning network with maximum likelihood and estimated weights based on linear Gaussian conditional relationship in the MV time-series. Considering the brain neuro-imaging signals as the multivariate data, we evaluated the performance of the proposed approach using the publicly available fMRI data-set and the results of the similar study which had evaluated popular network estimation approaches on the simulated fMRI data.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Simultaneous EEG Analysis and Feature Extraction Selection Based on Unsupervised Learning

Nächstes Kapitel Preference Prediction Based on Eye Movement Using Multi-layer Combinatorial Fusion

Lacasa, L., Nicosia, V., Latora, V.: Network structure of multivariate time series. Sci. Rep. 5, Article no. 15508 (2015)

Hu, Y., Zhao, H., Ai, X.: Inferring weighted directed association network from multivariate time series with a synthetic method of partial symbolic transfer entropy spectrum and granger causality. PLOS ONE 11(11), 1–25 (2016)

Zhang, J., Small, M.: Complex network from pseudoperiodic time series: topology versus dynamics. Phys. Rev. Lett. 96, Article no. 238701 (2006)

Gutin, G., Mansour, T., Severini, S.: A characterization of horizontal visibility graphs and combinatorics on words. Phys. A Stat. Mech. Appl. 390(12), 2421–2428 (2011)MathSciNetCrossRef

Anacleto, O., Queen, C., Albers, C.J.: Multivariate forecasting of road traffic flows in the presence of heteroscedasticity and measurement errors. J. R. Stat. Society. Ser. C (Appl. Stat.) 62(2), 251–270 (2013)MathSciNetCrossRef

Zhao, Z.Y., Xie, M., West, M.: Dynamic dependence networks: financial time series forecasting and portfolio decisions. Appl. Stoch. Model. Bus. Ind. 32(3), 311–332 (2016). asmb.2161MathSciNetCrossRef

Siebenhhner, F., Weiss, S.A., Coppola, R., Weinberger, D.R., Bassett, D.S.: Intra- and inter-frequency brain network structure in health and schizophrenia. PLOS ONE 8(8), 1–13 (2013)

White, B.R., et al.: Resting-state functional connectivity in the human brain revealed with diffuse optical tomography. NeuroImage 47(1), 148–156 (2009)CrossRef

Im, C.-H., Jung, Y.-J., Lee, S., Koh, D., Kim, D.-W., Kim, B.-M.: Estimation of directional coupling between cortical areas using near-infrared spectroscopy (NIRS). Opt. Express 18(6), 5730–5739 (2010)CrossRef

10.

Yuan, Z.: Combining independent component analysis and granger causality to investigate brain network dynamics with fNIRS measurements. Biomed. Opt. Express 4(11), 2629–2643 (2013)CrossRef

11.

Homae, F., et al.: Development of global cortical networks in early infancy. J. Neurosci. 30(14), 4877–4882 (2010)CrossRef

12.

David, O.: fMRI connectivity, meaning and empiricism comments on: Roebroeck et al. The identification of interacting networks in the brain using fMRI: model selection, causality and deconvolution. NeuroImage 58(2), 306–309 (2011)CrossRef

13.

Deshpande, G., Sathian, K., Hu, X.: Assessing and compensating for zero-lag correlation effects in time-lagged granger causality analysis of fMRI. IEEE Trans. Biomed. Eng. 57(6), 1446–1456 (2010)CrossRef

14.

Friston, K.: Dynamic causal modeling and granger causality comments on: the identification of interacting networks in the brain using fMRI: model selection, causality and deconvolution. NeuroImage 58(2), 303–305 (2011)CrossRef

15.

Smith, S.M., et al.: Network modelling methods for fMRI. NeuroImage 54(2), 875–891 (2011)CrossRef

16.

van Dellen, E., et al.: Minimum spanning tree analysis of the human connectome. Hum. Brain Mapp. 39(6), 2455–2471 (2018)CrossRef

17.

Stam, C.J., et al.: The relation between structural and functional connectivity patterns in complex brain networks. Int. J. Psychophysiol. 103, 149–160 (2016). Research on Brain Oscillations and Connectivity in A New Take-Off StateCrossRef

18.

Stam, C.J., Tewarie, P., Van Dellen, E., van Straaten, E.C.W., Hillebrand, A., Van Mieghem, P.: The trees and the forest: characterization of complex brain networks with minimum spanning trees. Int. J. Psychophysiol. 92(3), 129–138 (2014)CrossRef

19.

Tewarie, P., van Dellen, E., Hillebrand, A., Stam, C.J.: The minimum spanning tree: an unbiased method for brain network analysis. NeuroImage 104, 177–188 (2015)CrossRef

20.

Shmuel, A., Yacoub, E., Chaimow, D., Logothetis, N.K., Ugurbil, K.: Spatio-temporal point-spread function of fMRI signal in human gray matter at 7 tesla. NeuroImage 35(2), 539–552 (2007)CrossRef

21.

Tak, S., Kempny, A.M., Friston, K.J., Leff, A.P., Penny, W.D.: Dynamic causal modelling for functional near-infrared spectroscopy. NeuroImage 111(Supplement C), 338–349 (2015)CrossRef

Titel: Construction of Sparse Weighted Directed Network (SWDN) from the Multivariate Time-Series
verfasst von: Rahilsadat Hosseini
Feng Liu
Shouyi Wang
Verlag: Springer International Publishing
Buch: Brain Informatics
Print ISBN: 978-3-030-05586-8

Electronic ISBN: 978-3-030-05587-5

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-030-05587-5_26

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"