Inferring group-wise consistent multimodal brain networks via multi-view spectral clustering

Hanbo Chen; Kaiming Li; Dajiang Zhu; Tuo Zhang; Changfeng Jin; Lei Guo; Lingjiang Li; Tianming Liu

doi:10.1007/978-3-642-33454-2_37

Inferring group-wise consistent multimodal brain networks via multi-view spectral clustering

Hanbo Chen, Kaiming Li, Dajiang Zhu, Tuo Zhang, Changfeng Jin, Lei Guo, Lingjiang Li, Tianming Liu

School of Automation

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

5 Scopus citations

Abstract

Quantitative modeling and analysis of structural and functional brain networks based on diffusion tensor imaging (DTI)/functional MRI (fMRI) data has received extensive interest recently. However, the regularity of these structural or functional brain networks across multiple neuroimaging modalities and across individuals is largely unknown. This paper presents a novel approach to infer group-wise consistent brain sub-networks from multimodal DTI/fMRI datasets via multi-view spectral clustering of cortical networks, which were constructed on our recently developed and extensively validated large-scale cortical landmarks. We applied the proposed algorithm on 80 multimodal structural and functional brain networks of 40 healthy subjects, and obtained consistent multimodal brain sub-networks within the group. Our experiments demonstrated that the derived brain sub-networks have improved inter-modality and inter-subject consistency.

Original language	English
Title of host publication	Medical Image Computing and Computer-Assisted Intervention, MICCAI2012 - 15th International Conference, Proceedings
Editors	Nicholas Ayache, Herve Delingette, Polina Golland, Kensaku Mori
Publisher	Springer Verlag
Pages	297-304
Number of pages	8
ISBN (Print)	9783642334535
DOIs	https://doi.org/10.1007/978-3-642-33454-2_37
State	Published - 2012
Event	15th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2012 - Nice, France Duration: 1 Oct 2012 → 5 Oct 2012

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	7512 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	15th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2012
Country/Territory	France
City	Nice
Period	1/10/12 → 5/10/12

Keywords

DTI
fMRI
Multi-view clustering
Multimodal brain connectivity

Access to Document

10.1007/978-3-642-33454-2_37

Cite this

Chen, H., Li, K., Zhu, D., Zhang, T., Jin, C., Guo, L., Li, L., & Liu, T. (2012). Inferring group-wise consistent multimodal brain networks via multi-view spectral clustering. In N. Ayache, H. Delingette, P. Golland, & K. Mori (Eds.), Medical Image Computing and Computer-Assisted Intervention, MICCAI2012 - 15th International Conference, Proceedings (pp. 297-304). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7512 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-642-33454-2_37

Chen, Hanbo ; Li, Kaiming ; Zhu, Dajiang et al. / Inferring group-wise consistent multimodal brain networks via multi-view spectral clustering. Medical Image Computing and Computer-Assisted Intervention, MICCAI2012 - 15th International Conference, Proceedings. editor / Nicholas Ayache ; Herve Delingette ; Polina Golland ; Kensaku Mori. Springer Verlag, 2012. pp. 297-304 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Quantitative modeling and analysis of structural and functional brain networks based on diffusion tensor imaging (DTI)/functional MRI (fMRI) data has received extensive interest recently. However, the regularity of these structural or functional brain networks across multiple neuroimaging modalities and across individuals is largely unknown. This paper presents a novel approach to infer group-wise consistent brain sub-networks from multimodal DTI/fMRI datasets via multi-view spectral clustering of cortical networks, which were constructed on our recently developed and extensively validated large-scale cortical landmarks. We applied the proposed algorithm on 80 multimodal structural and functional brain networks of 40 healthy subjects, and obtained consistent multimodal brain sub-networks within the group. Our experiments demonstrated that the derived brain sub-networks have improved inter-modality and inter-subject consistency.",

keywords = "DTI, fMRI, Multi-view clustering, Multimodal brain connectivity",

author = "Hanbo Chen and Kaiming Li and Dajiang Zhu and Tuo Zhang and Changfeng Jin and Lei Guo and Lingjiang Li and Tianming Liu",

note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 2012.; 15th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2012 ; Conference date: 01-10-2012 Through 05-10-2012",

year = "2012",

doi = "10.1007/978-3-642-33454-2_37",

language = "英语",

isbn = "9783642334535",

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Chen, H, Li, K, Zhu, D, Zhang, T, Jin, C, Guo, L, Li, L & Liu, T 2012, Inferring group-wise consistent multimodal brain networks via multi-view spectral clustering. in N Ayache, H Delingette, P Golland & K Mori (eds), Medical Image Computing and Computer-Assisted Intervention, MICCAI2012 - 15th International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7512 LNCS, Springer Verlag, pp. 297-304, 15th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2012, Nice, France, 1/10/12. https://doi.org/10.1007/978-3-642-33454-2_37

Inferring group-wise consistent multimodal brain networks via multi-view spectral clustering. / Chen, Hanbo; Li, Kaiming; Zhu, Dajiang et al.
Medical Image Computing and Computer-Assisted Intervention, MICCAI2012 - 15th International Conference, Proceedings. ed. / Nicholas Ayache; Herve Delingette; Polina Golland; Kensaku Mori. Springer Verlag, 2012. p. 297-304 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7512 LNCS).

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

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T1 - Inferring group-wise consistent multimodal brain networks via multi-view spectral clustering

AU - Chen, Hanbo

AU - Li, Kaiming

AU - Zhu, Dajiang

AU - Zhang, Tuo

AU - Jin, Changfeng

AU - Guo, Lei

AU - Li, Lingjiang

AU - Liu, Tianming

N1 - Publisher Copyright: © Springer-Verlag Berlin Heidelberg 2012.

PY - 2012

Y1 - 2012

N2 - Quantitative modeling and analysis of structural and functional brain networks based on diffusion tensor imaging (DTI)/functional MRI (fMRI) data has received extensive interest recently. However, the regularity of these structural or functional brain networks across multiple neuroimaging modalities and across individuals is largely unknown. This paper presents a novel approach to infer group-wise consistent brain sub-networks from multimodal DTI/fMRI datasets via multi-view spectral clustering of cortical networks, which were constructed on our recently developed and extensively validated large-scale cortical landmarks. We applied the proposed algorithm on 80 multimodal structural and functional brain networks of 40 healthy subjects, and obtained consistent multimodal brain sub-networks within the group. Our experiments demonstrated that the derived brain sub-networks have improved inter-modality and inter-subject consistency.

AB - Quantitative modeling and analysis of structural and functional brain networks based on diffusion tensor imaging (DTI)/functional MRI (fMRI) data has received extensive interest recently. However, the regularity of these structural or functional brain networks across multiple neuroimaging modalities and across individuals is largely unknown. This paper presents a novel approach to infer group-wise consistent brain sub-networks from multimodal DTI/fMRI datasets via multi-view spectral clustering of cortical networks, which were constructed on our recently developed and extensively validated large-scale cortical landmarks. We applied the proposed algorithm on 80 multimodal structural and functional brain networks of 40 healthy subjects, and obtained consistent multimodal brain sub-networks within the group. Our experiments demonstrated that the derived brain sub-networks have improved inter-modality and inter-subject consistency.

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KW - fMRI

KW - Multi-view clustering

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SN - 9783642334535

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Medical Image Computing and Computer-Assisted Intervention, MICCAI2012 - 15th International Conference, Proceedings

A2 - Ayache, Nicholas

A2 - Delingette, Herve

A2 - Golland, Polina

A2 - Mori, Kensaku

PB - Springer Verlag

T2 - 15th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2012

Y2 - 1 October 2012 through 5 October 2012

ER -

Chen H, Li K, Zhu D, Zhang T, Jin C, Guo L et al. Inferring group-wise consistent multimodal brain networks via multi-view spectral clustering. In Ayache N, Delingette H, Golland P, Mori K, editors, Medical Image Computing and Computer-Assisted Intervention, MICCAI2012 - 15th International Conference, Proceedings. Springer Verlag. 2012. p. 297-304. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-33454-2_37

Inferring group-wise consistent multimodal brain networks via multi-view spectral clustering

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Keywords

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