Construction of sparse weighted directed network (SWDN) from the multivariate time-series

Rahilsadat Hosseini; Feng Liu; Shouyi Wang

doi:10.1007/978-3-030-05587-5_26

Construction of sparse weighted directed network (SWDN) from the multivariate time-series

Rahilsadat Hosseini, Feng Liu, Shouyi Wang

University of Texas at Arlington

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	Brain Informatics - International Conference, BI 2018, Proceedings
Editors	Yang Yang, Vicky Yamamoto, Shouyi Wang, Erick Jones, Jianzhong Su, Tom Mitchell, Leon Iasemidis
Pages	270-281
Number of pages	12
DOIs	https://doi.org/10.1007/978-3-030-05587-5_26
State	Published - 2018
Event	International Conference on Brain Informatics, BI 2018 - Arlington, United States Duration: 7 Dec 2018 → 9 Dec 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11309 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	International Conference on Brain Informatics, BI 2018
Country/Territory	United States
City	Arlington
Period	7/12/18 → 9/12/18

Keywords

Multivariate time-series
Sparse weighted directed network (SWDN)
fMRI

Access to Document

10.1007/978-3-030-05587-5_26

Cite this

Hosseini, R., Liu, F., & Wang, S. (2018). Construction of sparse weighted directed network (SWDN) from the multivariate time-series. In Y. Yang, V. Yamamoto, S. Wang, E. Jones, J. Su, T. Mitchell, & L. Iasemidis (Eds.), Brain Informatics - International Conference, BI 2018, Proceedings (pp. 270-281). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11309 LNAI). https://doi.org/10.1007/978-3-030-05587-5_26

Hosseini, Rahilsadat ; Liu, Feng ; Wang, Shouyi. / Construction of sparse weighted directed network (SWDN) from the multivariate time-series. Brain Informatics - International Conference, BI 2018, Proceedings. editor / Yang Yang ; Vicky Yamamoto ; Shouyi Wang ; Erick Jones ; Jianzhong Su ; Tom Mitchell ; Leon Iasemidis. 2018. pp. 270-281 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Construction of sparse weighted directed network (SWDN) from the multivariate time-series",

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.",

keywords = "Multivariate time-series, Sparse weighted directed network (SWDN), fMRI",

author = "Rahilsadat Hosseini and Feng Liu and Shouyi Wang",

note = "Publisher Copyright: {\textcopyright} 2018, Springer Nature Switzerland AG.; International Conference on Brain Informatics, BI 2018 ; Conference date: 07-12-2018 Through 09-12-2018",

year = "2018",

doi = "10.1007/978-3-030-05587-5_26",

language = "English",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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Hosseini, R, Liu, F & Wang, S 2018, Construction of sparse weighted directed network (SWDN) from the multivariate time-series. in Y Yang, V Yamamoto, S Wang, E Jones, J Su, T Mitchell & L Iasemidis (eds), Brain Informatics - International Conference, BI 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11309 LNAI, pp. 270-281, International Conference on Brain Informatics, BI 2018, Arlington, United States, 7/12/18. https://doi.org/10.1007/978-3-030-05587-5_26

Construction of sparse weighted directed network (SWDN) from the multivariate time-series. / Hosseini, Rahilsadat; Liu, Feng; Wang, Shouyi.
Brain Informatics - International Conference, BI 2018, Proceedings. ed. / Yang Yang; Vicky Yamamoto; Shouyi Wang; Erick Jones; Jianzhong Su; Tom Mitchell; Leon Iasemidis. 2018. p. 270-281 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11309 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Construction of sparse weighted directed network (SWDN) from the multivariate time-series

AU - Hosseini, Rahilsadat

AU - Liu, Feng

AU - Wang, Shouyi

PY - 2018

Y1 - 2018

N2 - 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.

AB - 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.

KW - Multivariate time-series

KW - Sparse weighted directed network (SWDN)

KW - fMRI

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 270

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BT - Brain Informatics - International Conference, BI 2018, Proceedings

A2 - Yang, Yang

A2 - Yamamoto, Vicky

A2 - Wang, Shouyi

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A2 - Mitchell, Tom

A2 - Iasemidis, Leon

T2 - International Conference on Brain Informatics, BI 2018

Y2 - 7 December 2018 through 9 December 2018

ER -

Hosseini R, Liu F, Wang S. Construction of sparse weighted directed network (SWDN) from the multivariate time-series. In Yang Y, Yamamoto V, Wang S, Jones E, Su J, Mitchell T, Iasemidis L, editors, Brain Informatics - International Conference, BI 2018, Proceedings. 2018. p. 270-281. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-05587-5_26

Construction of sparse weighted directed network (SWDN) from the multivariate time-series

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