Multimodal hyper-connectivity networks for MCI classification

Yang Li; Xinqiang Gao; Biao Jie; Pew Thian Yap; Min jeong Kim; Chong Yaw Wee; Dinggang Shen

doi:10.1007/978-3-319-66182-7_50

Multimodal hyper-connectivity networks for MCI classification

Yang Li, Xinqiang Gao, Biao Jie, Pew Thian Yap, Min jeong Kim, Chong Yaw Wee, Dinggang Shen

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

6 Citations (Scopus)

Abstract

Hyper-connectivity network is a network where every edge is connected to more than two nodes, and can be naturally denoted using a hyper-graph. Hyper-connectivity brain network, either based on structural or functional interactions among the brain regions, has been used for brain disease diagnosis. However, the conventional hyper-connectivity network is constructed solely based on single modality data, ignoring potential complementary information conveyed by other modalities. The integration of complementary information from multiple modalities has been shown to provide a more comprehensive representation about the brain disruptions. In this paper, a novel multimodal hyper-network modelling method was proposed for improving the diagnostic accuracy of mild cognitive impairment (MCI). Specifically, we first constructed a multimodal hyper-connectivity network by simultaneously considering information from diffusion tensor imaging and resting-state functional magnetic resonance imaging data. We then extracted different types of network features from the hyper-connectivity network, and further exploited a manifold regularized multi-task feature selection method to jointly select the most discriminative features. Our proposed multimodal hyper-connectivity network demonstrated a better MCI classification performance than the conventional single modality based hyper-connectivity networks.

Original language	English
Title of host publication	Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings
Editors	Maxime Descoteaux, Simon Duchesne, Alfred Franz, Pierre Jannin, D. Louis Collins, Lena Maier-Hein
Publisher	Springer Verlag
Pages	433-441
Number of pages	9
ISBN (Print)	9783319661810
DOIs	https://doi.org/10.1007/978-3-319-66182-7_50
Publication status	Published - 2017
Event	20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017 - Quebec City, Canada Duration: 2017 Sept 11 → 2017 Sept 13

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10433 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017
Country/Territory	Canada
City	Quebec City
Period	17/9/11 → 17/9/13

Bibliographical note

Publisher Copyright:
© 2017, Springer International Publishing AG.

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-319-66182-7_50

Cite this

Li, Y., Gao, X., Jie, B., Yap, P. T., Kim, M. J., Wee, C. Y., & Shen, D. (2017). Multimodal hyper-connectivity networks for MCI classification. In M. Descoteaux, S. Duchesne, A. Franz, P. Jannin, D. L. Collins, & L. Maier-Hein (Eds.), Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings (pp. 433-441). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10433 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-66182-7_50

Multimodal hyper-connectivity networks for MCI classification. / Li, Yang; Gao, Xinqiang; Jie, Biao et al.
Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings. ed. / Maxime Descoteaux; Simon Duchesne; Alfred Franz; Pierre Jannin; D. Louis Collins; Lena Maier-Hein. Springer Verlag, 2017. p. 433-441 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10433 LNCS).

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

Li, Y, Gao, X, Jie, B, Yap, PT, Kim, MJ, Wee, CY & Shen, D 2017, Multimodal hyper-connectivity networks for MCI classification. in M Descoteaux, S Duchesne, A Franz, P Jannin, DL Collins & L Maier-Hein (eds), Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10433 LNCS, Springer Verlag, pp. 433-441, 20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017, Quebec City, Canada, 17/9/11. https://doi.org/10.1007/978-3-319-66182-7_50

Li Y, Gao X, Jie B, Yap PT, Kim MJ, Wee CY et al. Multimodal hyper-connectivity networks for MCI classification. In Descoteaux M, Duchesne S, Franz A, Jannin P, Collins DL, Maier-Hein L, editors, Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings. Springer Verlag. 2017. p. 433-441. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-66182-7_50

Li, Yang ; Gao, Xinqiang ; Jie, Biao et al. / Multimodal hyper-connectivity networks for MCI classification. Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings. editor / Maxime Descoteaux ; Simon Duchesne ; Alfred Franz ; Pierre Jannin ; D. Louis Collins ; Lena Maier-Hein. Springer Verlag, 2017. pp. 433-441 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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Multimodal hyper-connectivity networks for MCI classification

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