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

自动化学院

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

5 引用（Scopus）

摘要

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.

源语言	英语
主期刊名	Medical Image Computing and Computer-Assisted Intervention, MICCAI2012 - 15th International Conference, Proceedings
编辑	Nicholas Ayache, Herve Delingette, Polina Golland, Kensaku Mori
出版商	Springer Verlag
页	297-304
页数	8
ISBN（印刷版）	9783642334535
DOI	https://doi.org/10.1007/978-3-642-33454-2_37
出版状态	已出版 - 2012
活动	15th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2012 - Nice, 法国期限: 1 10月 2012 → 5 10月 2012

出版系列

姓名	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
卷	7512 LNCS
ISSN（印刷版）	0302-9743
ISSN（电子版）	1611-3349

会议

会议	15th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2012
国家/地区	法国
市	Nice
时期	1/10/12 → 5/10/12

访问文件

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

其它文件与链接

链接到 Scopus 的出版物

引用此

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. 在 N. Ayache, H. Delingette, P. Golland, & K. Mori (编辑), Medical Image Computing and Computer-Assisted Intervention, MICCAI2012 - 15th International Conference, Proceedings (页码 297-304). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); 卷 7512 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-642-33454-2_37

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

@inproceedings{1ad96b4f0f3640c69ad0fe29949b5b6a,

title = "Inferring group-wise consistent multimodal brain networks via multi-view spectral clustering",

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",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "297--304",

editor = "Nicholas Ayache and Herve Delingette and Polina Golland and Kensaku Mori",

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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. 在 N Ayache, H Delingette, P Golland & K Mori (编辑), 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), 卷 7512 LNCS, Springer Verlag, 页码 297-304, 15th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2012, Nice, 法国, 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 等.
Medical Image Computing and Computer-Assisted Intervention, MICCAI2012 - 15th International Conference, Proceedings. 编辑 / Nicholas Ayache; Herve Delingette; Polina Golland; Kensaku Mori. Springer Verlag, 2012. 页码 297-304 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); 卷 7512 LNCS).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

TY - GEN

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.

KW - DTI

KW - fMRI

KW - Multi-view clustering

KW - Multimodal brain connectivity

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U2 - 10.1007/978-3-642-33454-2_37

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

M3 - 会议稿件

C2 - 23286143

AN - SCOPUS:84872909849

SN - 9783642334535

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

SP - 297

EP - 304

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 等. Inferring group-wise consistent multimodal brain networks via multi-view spectral clustering. 在 Ayache N, Delingette H, Golland P, Mori K, 编辑, Medical Image Computing and Computer-Assisted Intervention, MICCAI2012 - 15th International Conference, Proceedings. Springer Verlag. 2012. 页码 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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