Discriminant analysis of functional connectivity patterns on Grassmann manifold

The functional brain networks, extracted from fMRI images using independent component analysis, have been demonstrated informative for distinguishing brain states of cognitive function and brain disorders. Rather than analyzing each network encoded by a spatial independent component separately, we p...

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Published in:	NeuroImage (Orlando, Fla.) Vol. 56; no. 4; pp. 2058 - 2067
Main Authors:	Fan, Yong, Liu, Yong, Wu, Hong, Hao, Yihui, Liu, Haihong, Liu, Zhening, Jiang, Tianzi
Format:	Journal Article
Language:	English
Published:	United States Elsevier Inc 15.06.2011 Elsevier Limited
Subjects:	Adult Algorithms Brain - physiopathology Brain Mapping - methods Classification Correlation analysis Discriminant Analysis Female fMRI Functional connectivity patterns Grassmann manifold Humans Image Interpretation, Computer-Assisted - methods Magnetic Resonance Imaging Male Methods Neural Pathways - physiopathology NMR Nuclear magnetic resonance Resting-state Schizophrenia Schizophrenia - classification Schizophrenia - physiopathology Studies Young Adult Functional connectivity patterns Schizophrenia fMRI Resting-state Discriminant analysis Grassmann manifold
ISSN:	1053-8119, 1095-9572, 1095-9572
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Abstract	The functional brain networks, extracted from fMRI images using independent component analysis, have been demonstrated informative for distinguishing brain states of cognitive function and brain disorders. Rather than analyzing each network encoded by a spatial independent component separately, we propose a novel algorithm for discriminant analysis of functional brain networks jointly at an individual level. The functional brain networks of each individual are used as bases for a linear subspace, referred to as a functional connectivity pattern, which facilitates a comprehensive characterization of fMRI data. The functional connectivity patterns of different individuals are analyzed on the Grassmann manifold by adopting a principal angle based Riemannian distance. In conjunction with a support vector machine classifier, a forward component selection technique is proposed to select independent components for constructing the most discriminative functional connectivity pattern. The discriminant analysis method has been applied to an fMRI based schizophrenia study with 31 schizophrenia patients and 31 healthy individuals. The experimental results demonstrate that the proposed method not only achieves a promising classification performance for distinguishing schizophrenia patients from healthy controls, but also identifies discriminative functional brain networks that are informative for schizophrenia diagnosis. ► Automatically identify discriminative functional brain networks. ► Optimally combine brain networks on a Grassmann manifold for discriminative analysis. ► Demonstrate a functional connectivity pattern's discriminative performance.
AbstractList	The functional brain networks, extracted from fMRI images using independent component analysis, have been demonstrated informative for distinguishing brain states of cognitive function and brain disorders. Rather than analyzing each network encoded by a spatial independent component separately, we propose a novel algorithm for discriminant analysis of functional brain networks jointly at an individual level. The functional brain networks of each individual are used as bases for a linear subspace, referred to as a functional connectivity pattern, which facilitates a comprehensive characterization of fMRI data. The functional connectivity patterns of different individuals are analyzed on the Grassmann manifold by adopting a principal angle based Riemannian distance. In conjunction with a support vector machine classifier, a forward component selection technique is proposed to select independent components for constructing the most discriminative functional connectivity pattern. The discriminant analysis method has been applied to an fMRI based schizophrenia study with 31 schizophrenia patients and 31 healthy individuals. The experimental results demonstrate that the proposed method not only achieves a promising classification performance for distinguishing schizophrenia patients from healthy controls, but also identifies discriminative functional brain networks that are informative for schizophrenia diagnosis. ► Automatically identify discriminative functional brain networks. ► Optimally combine brain networks on a Grassmann manifold for discriminative analysis. ► Demonstrate a functional connectivity pattern's discriminative performance. The functional brain networks, extracted from fMRI images using independent component analysis, have been demonstrated informative for distinguishing brain states of cognitive function and brain disorders. Rather than analyzing each network encoded by a spatial independent component separately, we propose a novel algorithm for discriminant analysis of functional brain networks jointly at an individual level. The functional brain networks of each individual are used as bases for a linear subspace, referred to as a functional connectivity pattern, which facilitates a comprehensive characterization of fMRI data. The functional connectivity patterns of different individuals are analyzed on the Grassmann manifold by adopting a principal angle based Riemannian distance. In conjunction with a support vector machine classifier, a forward component selection technique is proposed to select independent components for constructing the most discriminative functional connectivity pattern. The discriminant analysis method has been applied to an fMRI based schizophrenia study with 31 schizophrenia patients and 31 healthy individuals. The experimental results demonstrate that the proposed method not only achieves a promising classification performance for distinguishing schizophrenia patients from healthy controls, but also identifies discriminative functional brain networks that are informative for schizophrenia diagnosis. The functional brain networks, extracted from fMRI images using independent component analysis, have been demonstrated informative for distinguishing brain states of cognitive function and brain disorders. Rather than analyzing each network encoded by a spatial independent component separately, we propose a novel algorithm for discriminant analysis of functional brain networks jointly at an individual level. The functional brain networks of each individual are used as bases for a linear subspace, referred to as a functional connectivity pattern, which facilitates a comprehensive characterization of fMRI data. The functional connectivity patterns of different individuals are analyzed on the Grassmann manifold by adopting a principal angle based Riemannian distance. In conjunction with a support vector machine classifier, a forward component selection technique is proposed to select independent components for constructing the most discriminative functional connectivity pattern. The discriminant analysis method has been applied to an fMRI based schizophrenia study with 31 schizophrenia patients and 31 healthy individuals. The experimental results demonstrate that the proposed method not only achieves a promising classification performance for distinguishing schizophrenia patients from healthy controls, but also identifies discriminative functional brain networks that are informative for schizophrenia diagnosis.The functional brain networks, extracted from fMRI images using independent component analysis, have been demonstrated informative for distinguishing brain states of cognitive function and brain disorders. Rather than analyzing each network encoded by a spatial independent component separately, we propose a novel algorithm for discriminant analysis of functional brain networks jointly at an individual level. The functional brain networks of each individual are used as bases for a linear subspace, referred to as a functional connectivity pattern, which facilitates a comprehensive characterization of fMRI data. The functional connectivity patterns of different individuals are analyzed on the Grassmann manifold by adopting a principal angle based Riemannian distance. In conjunction with a support vector machine classifier, a forward component selection technique is proposed to select independent components for constructing the most discriminative functional connectivity pattern. The discriminant analysis method has been applied to an fMRI based schizophrenia study with 31 schizophrenia patients and 31 healthy individuals. The experimental results demonstrate that the proposed method not only achieves a promising classification performance for distinguishing schizophrenia patients from healthy controls, but also identifies discriminative functional brain networks that are informative for schizophrenia diagnosis.
Author	Liu, Zhening Jiang, Tianzi Liu, Haihong Wu, Hong Liu, Yong Fan, Yong Hao, Yihui
Author_xml	– sequence: 1 givenname: Yong surname: Fan fullname: Fan, Yong email: yfan@ieee.org, yfan@nlpr.ia.ac.cn organization: LIAMA Center for Computational Medicine, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China – sequence: 2 givenname: Yong surname: Liu fullname: Liu, Yong organization: LIAMA Center for Computational Medicine, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China – sequence: 3 givenname: Hong surname: Wu fullname: Wu, Hong organization: School of Computer Science and Engineering,University of Electronic Science and Technology of China, Chengdu 611731, China – sequence: 4 givenname: Yihui surname: Hao fullname: Hao, Yihui organization: Institute of Mental Health, Second Xiangya Hospital, Central South university, Changsha 410011, China – sequence: 5 givenname: Haihong surname: Liu fullname: Liu, Haihong organization: Institute of Mental Health, Second Xiangya Hospital, Central South university, Changsha 410011, China – sequence: 6 givenname: Zhening surname: Liu fullname: Liu, Zhening email: zheningliu@yahoo.com.cn organization: Institute of Mental Health, Second Xiangya Hospital, Central South university, Changsha 410011, China – sequence: 7 givenname: Tianzi surname: Jiang fullname: Jiang, Tianzi email: jiangtz@nlpr.ia.ac.cn organization: LIAMA Center for Computational Medicine, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
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Copyright	2011 Elsevier Inc. Copyright © 2011 Elsevier Inc. All rights reserved. Copyright Elsevier Limited Jun 15, 2011
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Keywords	Functional connectivity patterns Schizophrenia fMRI Resting-state Discriminant analysis Grassmann manifold
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Snippet	The functional brain networks, extracted from fMRI images using independent component analysis, have been demonstrated informative for distinguishing brain...
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SubjectTerms	Adult Algorithms Brain - physiopathology Brain Mapping - methods Classification Correlation analysis Discriminant Analysis Female fMRI Functional connectivity patterns Grassmann manifold Humans Image Interpretation, Computer-Assisted - methods Magnetic Resonance Imaging Male Methods Neural Pathways - physiopathology NMR Nuclear magnetic resonance Resting-state Schizophrenia Schizophrenia - classification Schizophrenia - physiopathology Studies Young Adult
Title	Discriminant analysis of functional connectivity patterns on Grassmann manifold
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