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Identification of MCI individuals using structural and functional connectivity networks

NeuroImage (Orlando, Fla.), 2012-02, Vol.59 (3), p.2045-2056 [Peer Reviewed Journal]

2011 Elsevier Inc. ;Copyright © 2011 Elsevier Inc. All rights reserved. ;Copyright Elsevier Limited Feb 1, 2012 ;2011 Elsevier Inc. All rights reserved. 2011 ;ISSN: 1053-8119 ;EISSN: 1095-9572 ;DOI: 10.1016/j.neuroimage.2011.10.015 ;PMID: 22019883

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Title:
Identification of MCI individuals using structural and functional connectivity networks
Author: Wee, Chong-Yaw ; Yap, Pew-Thian ; Zhang, Daoqiang ; Denny, Kevin ; Browndyke, Jeffrey N. ; Potter, Guy G. ; Welsh-Bohmer, Kathleen A. ; Wang, Lihong ; Shen, Dinggang
Subjects: Aged ; Algorithms ; Alzheimer's disease ; Alzheimer's disease (AD) ; Artificial Intelligence ; Brain ; Brain - pathology ; Brain network analysis multiple-kernel Support Vector Machines (SVMs) ; Classification ; Cluster Analysis ; Cognitive ability ; Cognitive Dysfunction - diagnosis ; Cognitive Dysfunction - pathology ; Cohort Studies ; Data Interpretation, Statistical ; Diffusion Tensor Imaging ; Diffusion tensor imaging (DTI) ; Educational Status ; Female ; Humans ; Image Processing, Computer-Assisted ; Linear Models ; Magnetic Resonance Imaging ; Male ; Medical imaging ; Memory ; Middle Aged ; Mild cognitive impairment (MCI) ; Multimodality representation ; Nerve Net - pathology ; Neural Pathways - pathology ; Neuropsychological Tests ; NMR ; Nonlinear Dynamics ; Nuclear magnetic resonance ; Reproducibility of Results ; Resting-state functional magnetic resonance imaging (rs-fMRI) ; ROC Curve ; Support Vector Machine
Is Part Of: NeuroImage (Orlando, Fla.), 2012-02, Vol.59 (3), p.2045-2056
Description: Different imaging modalities provide essential complementary information that can be used to enhance our understanding of brain disorders. This study focuses on integrating multiple imaging modalities to identify individuals at risk for mild cognitive impairment (MCI). MCI, often an early stage of Alzheimer's disease (AD), is difficult to diagnose due to its very mild or insignificant symptoms of cognitive impairment. Recent emergence of brain network analysis has made characterization of neurological disorders at a whole-brain connectivity level possible, thus providing new avenues for brain diseases classification. Employing multiple-kernel Support Vector Machines (SVMs), we attempt to integrate information from diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (rs-fMRI) for improving classification performance. Our results indicate that the multimodality classification approach yields statistically significant improvement in accuracy over using each modality independently. The classification accuracy obtained by the proposed method is 96.3%, which is an increase of at least 7.4% from the single modality-based methods and the direct data fusion method. A cross-validation estimation of the generalization performance gives an area of 0.953 under the receiver operating characteristic (ROC) curve, indicating excellent diagnostic power. The multimodality classification approach hence allows more accurate early detection of brain abnormalities with greater sensitivity.
Publisher: United States: Elsevier Inc
Language: English
Identifier: ISSN: 1053-8119
EISSN: 1095-9572
DOI: 10.1016/j.neuroimage.2011.10.015
PMID: 22019883
Source: ProQuest One Psychology
AUTh Library subscriptions: ProQuest Central
MEDLINE
Alma/SFX Local Collection

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Identification of MCI individuals using structural and functional connectivity networks

2011 Elsevier Inc. ;Copyright © 2011 Elsevier Inc. All rights reserved. ;Copyright Elsevier Limited Feb 1, 2012 ;2011 Elsevier Inc. All rights reserved. 2011 ;ISSN: 1053-8119 ;EISSN: 1095-9572 ;DOI: 10.1016/j.neuroimage.2011.10.015 ;PMID: 22019883

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