SEMI-Supervised learning of brain functional networks

Yuhui Du; Jing Sui; Qingbao Yu; Hao He; Vince D. Calhoun

doi:10.1109/isbi.2014.6867794

SEMI-Supervised learning of brain functional networks

Yuhui Du, Jing Sui, Qingbao Yu, Hao He, Vince D. Calhoun

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

3 Scopus citations

Abstract

Identification of subject-specific brain functional networks of interest is of great importance in fMRI based brain network analysis. In this study, a novel method is proposed to identify subject-specific brain functional networks using a graph theory based semi-supervised learning technique by incorporating not only prior information of the network to be identified as similarly used in seed region based correlation analysis (SCA) but also background information, which leads to robust performance for fMRI data with low signal noise ratio (SNR). Comparison experiments on both simulated and real fMRI data demonstrate that our method is more robust and accurate for identification of known brain functional networks than SCA, blind independent component analysis (ICA), and clustering based methods including Ncut and Kmeans.

Original language	English (US)
Title of host publication	2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1-4
Number of pages	4
ISBN (Electronic)	9781467319591
DOIs	https://doi.org/10.1109/isbi.2014.6867794
State	Published - Jul 29 2014
Externally published	Yes
Event	2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014 - Beijing, China Duration: Apr 29 2014 → May 2 2014

Publication series

Name	2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014

Other

Other	2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014
Country/Territory	China
City	Beijing
Period	4/29/14 → 5/2/14

Keywords

Brain functional network
Clustering
Independent component analysis
Semi-supervised learning

ASJC Scopus subject areas

Biomedical Engineering
Radiology Nuclear Medicine and imaging

Access to Document

10.1109/isbi.2014.6867794

Cite this

Du, Y., Sui, J., Yu, Q., He, H., & Calhoun, V. D. (2014). SEMI-Supervised learning of brain functional networks. In 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014 (pp. 1-4). [6867794] (2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/isbi.2014.6867794

SEMI-Supervised learning of brain functional networks. / Du, Yuhui; Sui, Jing; Yu, Qingbao et al.

2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014. Institute of Electrical and Electronics Engineers Inc., 2014. p. 1-4 6867794 (2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014).

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

Du, Y, Sui, J, Yu, Q, He, H & Calhoun, VD 2014, SEMI-Supervised learning of brain functional networks. in 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014., 6867794, 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014, Institute of Electrical and Electronics Engineers Inc., pp. 1-4, 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014, Beijing, China, 4/29/14. https://doi.org/10.1109/isbi.2014.6867794

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abstract = "Identification of subject-specific brain functional networks of interest is of great importance in fMRI based brain network analysis. In this study, a novel method is proposed to identify subject-specific brain functional networks using a graph theory based semi-supervised learning technique by incorporating not only prior information of the network to be identified as similarly used in seed region based correlation analysis (SCA) but also background information, which leads to robust performance for fMRI data with low signal noise ratio (SNR). Comparison experiments on both simulated and real fMRI data demonstrate that our method is more robust and accurate for identification of known brain functional networks than SCA, blind independent component analysis (ICA), and clustering based methods including Ncut and Kmeans.",

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author = "Yuhui Du and Jing Sui and Qingbao Yu and Hao He and Calhoun, {Vince D.}",

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AB - Identification of subject-specific brain functional networks of interest is of great importance in fMRI based brain network analysis. In this study, a novel method is proposed to identify subject-specific brain functional networks using a graph theory based semi-supervised learning technique by incorporating not only prior information of the network to be identified as similarly used in seed region based correlation analysis (SCA) but also background information, which leads to robust performance for fMRI data with low signal noise ratio (SNR). Comparison experiments on both simulated and real fMRI data demonstrate that our method is more robust and accurate for identification of known brain functional networks than SCA, blind independent component analysis (ICA), and clustering based methods including Ncut and Kmeans.

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SEMI-Supervised learning of brain functional networks

Abstract

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Keywords

ASJC Scopus subject areas

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