Fusion of high-order and low-order effective connectivity networks for MCI classification

Yang Li; Jingyu Liu; Ke Li; Pew Thian Yap; Minjeong Kim; Chong Yaw Wee; Dinggang Shen

doi:10.1007/978-3-319-67389-9_36

Fusion of high-order and low-order effective connectivity networks for MCI classification

Yang Li, Jingyu Liu, Ke Li, Pew Thian Yap, Minjeong Kim, Chong Yaw Wee, Dinggang Shen

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

1 Citation (Scopus)

Abstract

Functional connectivity network derived from resting-state fMRI data has been found as effective biomarkers for identifying patients with mild cognitive impairment from healthy elderly. However, the ordinary functional connectivity network is essentially a low-order network with the assumption that the brain is static during the entire scanning period, ignoring the temporal variations among correlations derived from brain region pairs. To overcome this weakness, we proposed a new type of high-order network to more accurately describe the relationship of temporal variations among brain regions. Specifically, instead of the commonly used undirected pairwise Pearson’s correlation coefficient, we first estimated the low-order effective connectivity network based on a novel sparse regression algorithm. By using the similar approach, we then constructed the high-order effective connectivity network from low-order connectivity to incorporate signal flow information among the brain regions. We finally combined the low-order and the high-order effective connectivity networks using two decision trees for MCI classification and experimental results obtained demonstrate the superiority of the proposed method over the conventional undirected low-order and high-order functional connectivity networks, as well as the low-order and high-order effective connectivity networks when they were used separately.

Original language	English
Title of host publication	Machine Learning in Medical Imaging - 8th International Workshop, MLMI 2017, Held in Conjunction with MICCAI 2017, Proceedings
Editors	Yinghuan Shi, Heung-Il Suk, Kenji Suzuki, Qian Wang
Publisher	Springer Verlag
Pages	307-315
Number of pages	9
ISBN (Print)	9783319673882
DOIs	https://doi.org/10.1007/978-3-319-67389-9_36
Publication status	Published - 2017
Event	8th International Workshop on Machine Learning in Medical Imaging, MLMI 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 10 → 2017 Sept 10

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10541 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	8th International Workshop on Machine Learning in Medical Imaging, MLMI 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	17/9/10 → 17/9/10

Bibliographical note

Publisher Copyright:
© 2017, Springer International Publishing AG.

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-319-67389-9_36

Cite this

Li, Y., Liu, J., Li, K., Yap, P. T., Kim, M., Wee, C. Y., & Shen, D. (2017). Fusion of high-order and low-order effective connectivity networks for MCI classification. In Y. Shi, H.-I. Suk, K. Suzuki, & Q. Wang (Eds.), Machine Learning in Medical Imaging - 8th International Workshop, MLMI 2017, Held in Conjunction with MICCAI 2017, Proceedings (pp. 307-315). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10541 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-67389-9_36

Fusion of high-order and low-order effective connectivity networks for MCI classification. / Li, Yang; Liu, Jingyu; Li, Ke et al.
Machine Learning in Medical Imaging - 8th International Workshop, MLMI 2017, Held in Conjunction with MICCAI 2017, Proceedings. ed. / Yinghuan Shi; Heung-Il Suk; Kenji Suzuki; Qian Wang. Springer Verlag, 2017. p. 307-315 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10541 LNCS).

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

Li, Y, Liu, J, Li, K, Yap, PT, Kim, M, Wee, CY & Shen, D 2017, Fusion of high-order and low-order effective connectivity networks for MCI classification. in Y Shi, H-I Suk, K Suzuki & Q Wang (eds), Machine Learning in Medical Imaging - 8th International Workshop, MLMI 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. 10541 LNCS, Springer Verlag, pp. 307-315, 8th International Workshop on Machine Learning in Medical Imaging, MLMI 2017 held in conjunction with the 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017, Quebec City, Canada, 17/9/10. https://doi.org/10.1007/978-3-319-67389-9_36

Li Y, Liu J, Li K, Yap PT, Kim M, Wee CY et al. Fusion of high-order and low-order effective connectivity networks for MCI classification. In Shi Y, Suk HI, Suzuki K, Wang Q, editors, Machine Learning in Medical Imaging - 8th International Workshop, MLMI 2017, Held in Conjunction with MICCAI 2017, Proceedings. Springer Verlag. 2017. p. 307-315. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-67389-9_36

Li, Yang ; Liu, Jingyu ; Li, Ke et al. / Fusion of high-order and low-order effective connectivity networks for MCI classification. Machine Learning in Medical Imaging - 8th International Workshop, MLMI 2017, Held in Conjunction with MICCAI 2017, Proceedings. editor / Yinghuan Shi ; Heung-Il Suk ; Kenji Suzuki ; Qian Wang. Springer Verlag, 2017. pp. 307-315 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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Fusion of high-order and low-order effective connectivity networks for MCI classification

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