Functional connectivity network fusion with dynamic thresholding for MCI diagnosis

Xi Yang; Yan Jin; Xiaobo Chen; Han Zhang; Gang Li; Dinggang Shen

doi:10.1007/978-3-319-47157-0_30

Functional connectivity network fusion with dynamic thresholding for MCI diagnosis

Xi Yang, Yan Jin, Xiaobo Chen, Han Zhang, Gang Li, Dinggang Shen

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

11 Citations (Scopus)

Abstract

The resting-state functional MRI (rs-fMRI) has been demonstrated as a valuable neuroimaging tool to identify mild cognitive impairment (MCI) patients. Previous studies showed network breakdown in MCI patients with thresholded rs-fMRI connectivity networks. Recently, machine learning techniques have assisted MCI diagnosis by integrating information from multiple networks constructed with a range of thresholds. However, due to the difficulty of searching optimal thresholds, they are often predetermined and uniformly applied to the entire network. Here, we propose an element-wise thresholding strategy to dynamically construct multiple functional networks, i.e., using possibly different thresholds for different elements in the connectivity matrix. These dynamically generated networks are then integrated with a network fusion scheme to capture their common and complementary information. Finally, the features extracted from the fused network are fed into support vector machine (SVM) for MCI diagnosis. Compared to the previous methods, our proposed framework can greatly improve MCI classification performance.

Original language	English
Title of host publication	Machine Learning in Medical Imaging - 7th International Workshop, MLMI 2016 held in conjunction with MICCAI 2016, Proceedings
Editors	Li Wang, Heung-Il Suk, Yinghuan Shi, Ehsan Adeli, Qian Wang
Publisher	Springer Verlag
Pages	246-253
Number of pages	8
ISBN (Print)	9783319471563
DOIs	https://doi.org/10.1007/978-3-319-47157-0_30
Publication status	Published - 2016
Event	7th International Workshop on Machine Learning in Medical Imaging, MLMI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016 - Athens, Greece Duration: 2016 Oct 17 → 2016 Oct 17

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10019 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	7th International Workshop on Machine Learning in Medical Imaging, MLMI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016
Country/Territory	Greece
City	Athens
Period	16/10/17 → 16/10/17

Bibliographical note

Publisher Copyright:
© Springer International Publishing AG 2016.

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-319-47157-0_30

Cite this

Yang, X., Jin, Y., Chen, X., Zhang, H., Li, G., & Shen, D. (2016). Functional connectivity network fusion with dynamic thresholding for MCI diagnosis. In L. Wang, H.-I. Suk, Y. Shi, E. Adeli, & Q. Wang (Eds.), Machine Learning in Medical Imaging - 7th International Workshop, MLMI 2016 held in conjunction with MICCAI 2016, Proceedings (pp. 246-253). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10019 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-47157-0_30

Functional connectivity network fusion with dynamic thresholding for MCI diagnosis. / Yang, Xi; Jin, Yan; Chen, Xiaobo et al.
Machine Learning in Medical Imaging - 7th International Workshop, MLMI 2016 held in conjunction with MICCAI 2016, Proceedings. ed. / Li Wang; Heung-Il Suk; Yinghuan Shi; Ehsan Adeli; Qian Wang. Springer Verlag, 2016. p. 246-253 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10019 LNCS).

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

Yang, X, Jin, Y, Chen, X, Zhang, H, Li, G & Shen, D 2016, Functional connectivity network fusion with dynamic thresholding for MCI diagnosis. in L Wang, H-I Suk, Y Shi, E Adeli & Q Wang (eds), Machine Learning in Medical Imaging - 7th International Workshop, MLMI 2016 held in conjunction with MICCAI 2016, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10019 LNCS, Springer Verlag, pp. 246-253, 7th International Workshop on Machine Learning in Medical Imaging, MLMI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016, Athens, Greece, 16/10/17. https://doi.org/10.1007/978-3-319-47157-0_30

Yang X, Jin Y, Chen X, Zhang H, Li G, Shen D. Functional connectivity network fusion with dynamic thresholding for MCI diagnosis. In Wang L, Suk HI, Shi Y, Adeli E, Wang Q, editors, Machine Learning in Medical Imaging - 7th International Workshop, MLMI 2016 held in conjunction with MICCAI 2016, Proceedings. Springer Verlag. 2016. p. 246-253. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-47157-0_30

Yang, Xi ; Jin, Yan ; Chen, Xiaobo et al. / Functional connectivity network fusion with dynamic thresholding for MCI diagnosis. Machine Learning in Medical Imaging - 7th International Workshop, MLMI 2016 held in conjunction with MICCAI 2016, Proceedings. editor / Li Wang ; Heung-Il Suk ; Yinghuan Shi ; Ehsan Adeli ; Qian Wang. Springer Verlag, 2016. pp. 246-253 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "The resting-state functional MRI (rs-fMRI) has been demonstrated as a valuable neuroimaging tool to identify mild cognitive impairment (MCI) patients. Previous studies showed network breakdown in MCI patients with thresholded rs-fMRI connectivity networks. Recently, machine learning techniques have assisted MCI diagnosis by integrating information from multiple networks constructed with a range of thresholds. However, due to the difficulty of searching optimal thresholds, they are often predetermined and uniformly applied to the entire network. Here, we propose an element-wise thresholding strategy to dynamically construct multiple functional networks, i.e., using possibly different thresholds for different elements in the connectivity matrix. These dynamically generated networks are then integrated with a network fusion scheme to capture their common and complementary information. Finally, the features extracted from the fused network are fed into support vector machine (SVM) for MCI diagnosis. Compared to the previous methods, our proposed framework can greatly improve MCI classification performance.",

author = "Xi Yang and Yan Jin and Xiaobo Chen and Han Zhang and Gang Li and Dinggang Shen",

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AU - Yang, Xi

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N1 - Publisher Copyright: © Springer International Publishing AG 2016.

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N2 - The resting-state functional MRI (rs-fMRI) has been demonstrated as a valuable neuroimaging tool to identify mild cognitive impairment (MCI) patients. Previous studies showed network breakdown in MCI patients with thresholded rs-fMRI connectivity networks. Recently, machine learning techniques have assisted MCI diagnosis by integrating information from multiple networks constructed with a range of thresholds. However, due to the difficulty of searching optimal thresholds, they are often predetermined and uniformly applied to the entire network. Here, we propose an element-wise thresholding strategy to dynamically construct multiple functional networks, i.e., using possibly different thresholds for different elements in the connectivity matrix. These dynamically generated networks are then integrated with a network fusion scheme to capture their common and complementary information. Finally, the features extracted from the fused network are fed into support vector machine (SVM) for MCI diagnosis. Compared to the previous methods, our proposed framework can greatly improve MCI classification performance.

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Functional connectivity network fusion with dynamic thresholding for MCI diagnosis

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