Structural connectivity guided sparse effective connectivity for MCI identification

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

doi:10.1007/978-3-319-67389-9_35

Structural connectivity guided sparse effective connectivity for MCI identification

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

^*Corresponding author for this work

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

4 Citations (Scopus)

Abstract

Recent advances in network modelling techniques have enabled the study of neurological disorders at a whole-brain level based on functional connectivity inferred from resting-state magnetic resonance imaging (rs-fMRI) scan possible. However, constructing a directed effective connectivity, which provides a more comprehensive characterization of functional interactions among the brain regions, is still a challenging task particularly when the ultimate goal is to identify disease associated brain functional interaction anomalies. In this paper, we propose a novel method for inferring effective connectivity from multimodal neuroimaging data for brain disease classification. Specifically, we apply a newly devised weighted sparse regression model on rs-fMRI data to determine the network structure of effective connectivity with the guidance from diffusion tensor imaging (DTI) data. We further employ a regression algorithm to estimate the effective connectivity strengths based on the previously identified network structure. We finally utilize a bagging classifier to evaluate the performance of the proposed sparse effective connectivity network through identifying mild cognitive impairment from healthy aging.

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	299-306
Number of pages	8
ISBN (Print)	9783319673882
DOIs	https://doi.org/10.1007/978-3-319-67389-9_35
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_35

Cite this

Li, Y., Liu, J., Luo, M., Li, K., Yap, P. T., Kim, M., Wee, C. Y., & Shen, D. (2017). Structural connectivity guided sparse effective connectivity for MCI identification. 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. 299-306). (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_35

Structural connectivity guided sparse effective connectivity for MCI identification. / Li, Yang; Liu, Jingyu; Luo, Meilin 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. 299-306 (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, Luo, M, Li, K, Yap, PT, Kim, M, Wee, CY & Shen, D 2017, Structural connectivity guided sparse effective connectivity for MCI identification. 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. 299-306, 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_35

Li Y, Liu J, Luo M, Li K, Yap PT, Kim M et al. Structural connectivity guided sparse effective connectivity for MCI identification. 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. 299-306. (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_35

Li, Yang ; Liu, Jingyu ; Luo, Meilin et al. / Structural connectivity guided sparse effective connectivity for MCI identification. 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. 299-306 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Recent advances in network modelling techniques have enabled the study of neurological disorders at a whole-brain level based on functional connectivity inferred from resting-state magnetic resonance imaging (rs-fMRI) scan possible. However, constructing a directed effective connectivity, which provides a more comprehensive characterization of functional interactions among the brain regions, is still a challenging task particularly when the ultimate goal is to identify disease associated brain functional interaction anomalies. In this paper, we propose a novel method for inferring effective connectivity from multimodal neuroimaging data for brain disease classification. Specifically, we apply a newly devised weighted sparse regression model on rs-fMRI data to determine the network structure of effective connectivity with the guidance from diffusion tensor imaging (DTI) data. We further employ a regression algorithm to estimate the effective connectivity strengths based on the previously identified network structure. We finally utilize a bagging classifier to evaluate the performance of the proposed sparse effective connectivity network through identifying mild cognitive impairment from healthy aging.",

author = "Yang Li and Jingyu Liu and Meilin Luo and Ke Li and Yap, \{Pew Thian\} and Minjeong Kim and Wee, \{Chong Yaw\} and Dinggang Shen",

note = "Publisher Copyright: {\textcopyright} 2017, Springer International Publishing AG.; 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 ; Conference date: 10-09-2017 Through 10-09-2017",

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AU - Liu, Jingyu

AU - Luo, Meilin

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AU - Kim, Minjeong

AU - Wee, Chong Yaw

AU - Shen, Dinggang

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AB - Recent advances in network modelling techniques have enabled the study of neurological disorders at a whole-brain level based on functional connectivity inferred from resting-state magnetic resonance imaging (rs-fMRI) scan possible. However, constructing a directed effective connectivity, which provides a more comprehensive characterization of functional interactions among the brain regions, is still a challenging task particularly when the ultimate goal is to identify disease associated brain functional interaction anomalies. In this paper, we propose a novel method for inferring effective connectivity from multimodal neuroimaging data for brain disease classification. Specifically, we apply a newly devised weighted sparse regression model on rs-fMRI data to determine the network structure of effective connectivity with the guidance from diffusion tensor imaging (DTI) data. We further employ a regression algorithm to estimate the effective connectivity strengths based on the previously identified network structure. We finally utilize a bagging classifier to evaluate the performance of the proposed sparse effective connectivity network through identifying mild cognitive impairment from healthy aging.

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ER -

Structural connectivity guided sparse effective connectivity for MCI identification

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