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 proceedingConference contribution

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 languageEnglish
Title of host publicationMachine Learning in Medical Imaging - 8th International Workshop, MLMI 2017, Held in Conjunction with MICCAI 2017, Proceedings
PublisherSpringer Verlag
Pages307-315
Number of pages9
Volume10541 LNCS
ISBN (Print)9783319673882
DOIs
Publication statusPublished - 2017
Externally publishedYes
Event8th 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 Sep 102017 Sep 10

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10541 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other8th 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
CountryCanada
CityQuebec City
Period17/9/1017/9/10

Fingerprint

Network Connectivity
Brain
Fusion
Fusion reactions
Higher Order
Biomarkers
Decision trees
Pearson Correlation
Functional Magnetic Resonance Imaging
Scanning
Information Flow
Decision tree
Correlation coefficient
Pairwise
Connectivity
Regression
Entire
Experimental Results
Demonstrate

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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 Machine Learning in Medical Imaging - 8th International Workshop, MLMI 2017, Held in Conjunction with MICCAI 2017, Proceedings (Vol. 10541 LNCS, 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; Yap, Pew Thian; Kim, Minjeong; Wee, Chong Yaw; Shen, Dinggang.

Machine Learning in Medical Imaging - 8th International Workshop, MLMI 2017, Held in Conjunction with MICCAI 2017, Proceedings. Vol. 10541 LNCS 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 proceedingConference 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 Machine Learning in Medical Imaging - 8th International Workshop, MLMI 2017, Held in Conjunction with MICCAI 2017, Proceedings. vol. 10541 LNCS, 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 Machine Learning in Medical Imaging - 8th International Workshop, MLMI 2017, Held in Conjunction with MICCAI 2017, Proceedings. Vol. 10541 LNCS. Springer Verlag. 2017. p. 307-315. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-67389-9_36
Li, Yang ; Liu, Jingyu ; Li, Ke ; Yap, Pew Thian ; Kim, Minjeong ; Wee, Chong Yaw ; Shen, Dinggang. / 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. Vol. 10541 LNCS 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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