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Connectivity Strength-Weighted Sparse Group Representation-Based Brain Network Construction for MCI Classification

Authors
Yu, RenpingZhang, HanAn, LeChen, XiaoboWei, ZhihuiShen, Dinggang
Issue Date
5월-2017
Publisher
WILEY
Keywords
brain network; sparse representation; functional connectivity; mild cognitive impairment (MCI); disease classification
Citation
HUMAN BRAIN MAPPING, v.38, no.5, pp.2370 - 2383
Indexed
SCIE
SCOPUS
Journal Title
HUMAN BRAIN MAPPING
Volume
38
Number
5
Start Page
2370
End Page
2383
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/83707
DOI
10.1002/hbm.23524
ISSN
1065-9471
Abstract
Brain functional network analysis has shown great potential in understanding brain functions and also in identifying biomarkers for brain diseases, such as Alzheimer's disease (AD) and its early stage, mild cognitive impairment (MCI). In these applications, accurate construction of biologically meaningful brain network is critical. Sparse learning has been widely used for brain network construction; however, its l(1)-norm penalty simply penalizes each edge of a brain network equally, without considering the original connectivity strength which is one of the most important inherent linkwise characters. Besides, based on the similarity of the linkwise connectivity, brain network shows prominent group structure (i.e., a set of edges sharing similar attributes). In this article, we propose a novel brain functional network modeling framework with a "connectivity strength-weighted sparse group constraint." In particular, the network modeling can be optimized by considering both raw connectivity strength and its group structure, without losing the merit of sparsity. Our proposed method is applied to MCI classification, a challenging task for early AD diagnosis. Experimental results based on the resting-state functional MRI, from 50 MCI patients and 49 healthy controls, show that our proposed method is more effective (i.e., achieving a significantly higher classification accuracy, 84.8%) than other competing methods (e.g., sparse representation, accuracy -65.6%). Post hoc inspection of the informative features further shows more biologically meaningful brain functional connectivities obtained by our proposed method. (C) 2017 Wiley Periodicals, Inc.
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