Consciousness level and recovery outcome prediction using high-order brain functional connectivity network

Xiuyi Jia; Han Zhang; Ehsan Adeli; Dinggang Shen

doi:10.1007/978-3-319-67159-8_3

Consciousness level and recovery outcome prediction using high-order brain functional connectivity network

Xiuyi Jia, Han Zhang, Ehsan Adeli, Dinggang Shen^*

^*Corresponding author for this work

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

5 Citations (Scopus)

Abstract

Based on the neuroimaging data from a large set of acquired brain injury patients, we investigate the feasibility of using machine learning for automatic prediction of individual consciousness level. Rather than using the traditional Pearson’s correlation-based brain functional network, which measures only the simple temporal synchronization of the BOLD signals from each pair of brain regions, we construct a high-order brain functional network that is capable of characterizing topographical information-based high-level functional associations among brain regions. In such a high-order brain network, each node represents the community of a brain region, described by a set of this region’s low-order functional associations with other brain regions, and each edge characterizes topographical similarity between a pair of such communities. Experimental results show that the high-order brain functional network enables a significant better classification for consciousness level and recovery outcome prediction.

Original language	English
Title of host publication	Connectomics in NeuroImaging - 1st International Workshop, CNI 2017 Held in Conjunction with MICCAI 2017, Proceedings
Editors	Leonardo Bonilha, Guorong Wu, Paul Laurienti, Brent C. Munsell
Publisher	Springer Verlag
Pages	17-24
Number of pages	8
ISBN (Print)	9783319671581
DOIs	https://doi.org/10.1007/978-3-319-67159-8_3
Publication status	Published - 2017
Event	1st International Workshop on Connectomics in NeuroImaging, CNI 2017 held in conjunction with the 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017 - Quebec City, Canada Duration: 2017 Sept 14 → 2017 Sept 14

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10511 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	1st International Workshop on Connectomics in NeuroImaging, CNI 2017 held in conjunction with the 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017
Country/Territory	Canada
City	Quebec City
Period	17/9/14 → 17/9/14

Bibliographical note

Publisher Copyright:
© 2017, Springer International Publishing AG.

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-319-67159-8_3

Cite this

Jia, X., Zhang, H., Adeli, E., & Shen, D. (2017). Consciousness level and recovery outcome prediction using high-order brain functional connectivity network. In L. Bonilha, G. Wu, P. Laurienti, & B. C. Munsell (Eds.), Connectomics in NeuroImaging - 1st International Workshop, CNI 2017 Held in Conjunction with MICCAI 2017, Proceedings (pp. 17-24). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10511 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-67159-8_3

Consciousness level and recovery outcome prediction using high-order brain functional connectivity network. / Jia, Xiuyi; Zhang, Han; Adeli, Ehsan et al.
Connectomics in NeuroImaging - 1st International Workshop, CNI 2017 Held in Conjunction with MICCAI 2017, Proceedings. ed. / Leonardo Bonilha; Guorong Wu; Paul Laurienti; Brent C. Munsell. Springer Verlag, 2017. p. 17-24 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10511 LNCS).

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

Jia, X, Zhang, H, Adeli, E & Shen, D 2017, Consciousness level and recovery outcome prediction using high-order brain functional connectivity network. in L Bonilha, G Wu, P Laurienti & BC Munsell (eds), Connectomics in NeuroImaging - 1st International Workshop, CNI 2017 Held in Conjunction with MICCAI 2017, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10511 LNCS, Springer Verlag, pp. 17-24, 1st International Workshop on Connectomics in NeuroImaging, CNI 2017 held in conjunction with the 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017, Quebec City, Canada, 17/9/14. https://doi.org/10.1007/978-3-319-67159-8_3

Jia X, Zhang H, Adeli E, Shen D. Consciousness level and recovery outcome prediction using high-order brain functional connectivity network. In Bonilha L, Wu G, Laurienti P, Munsell BC, editors, Connectomics in NeuroImaging - 1st International Workshop, CNI 2017 Held in Conjunction with MICCAI 2017, Proceedings. Springer Verlag. 2017. p. 17-24. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-67159-8_3

Jia, Xiuyi ; Zhang, Han ; Adeli, Ehsan et al. / Consciousness level and recovery outcome prediction using high-order brain functional connectivity network. Connectomics in NeuroImaging - 1st International Workshop, CNI 2017 Held in Conjunction with MICCAI 2017, Proceedings. editor / Leonardo Bonilha ; Guorong Wu ; Paul Laurienti ; Brent C. Munsell. Springer Verlag, 2017. pp. 17-24 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Based on the neuroimaging data from a large set of acquired brain injury patients, we investigate the feasibility of using machine learning for automatic prediction of individual consciousness level. Rather than using the traditional Pearson{\textquoteright}s correlation-based brain functional network, which measures only the simple temporal synchronization of the BOLD signals from each pair of brain regions, we construct a high-order brain functional network that is capable of characterizing topographical information-based high-level functional associations among brain regions. In such a high-order brain network, each node represents the community of a brain region, described by a set of this region{\textquoteright}s low-order functional associations with other brain regions, and each edge characterizes topographical similarity between a pair of such communities. Experimental results show that the high-order brain functional network enables a significant better classification for consciousness level and recovery outcome prediction.",

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AB - Based on the neuroimaging data from a large set of acquired brain injury patients, we investigate the feasibility of using machine learning for automatic prediction of individual consciousness level. Rather than using the traditional Pearson’s correlation-based brain functional network, which measures only the simple temporal synchronization of the BOLD signals from each pair of brain regions, we construct a high-order brain functional network that is capable of characterizing topographical information-based high-level functional associations among brain regions. In such a high-order brain network, each node represents the community of a brain region, described by a set of this region’s low-order functional associations with other brain regions, and each edge characterizes topographical similarity between a pair of such communities. Experimental results show that the high-order brain functional network enables a significant better classification for consciousness level and recovery outcome prediction.

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Consciousness level and recovery outcome prediction using high-order brain functional connectivity network

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