NDLI: Hierarchical anatomical brain networks for MCI prediction by partial least square analysis

Content Provider	IEEE Xplore Digital Library
Author	Luping Zhou Yaping Wang Yang Li Pew-Thian Yap Dinggang Shen
Copyright Year	2011
Description	Author affiliation: University of North Carolina at Chapel Hill, U.S.A (Luping Zhou; Yang Li; Pew-Thian Yap; Dinggang Shen) \|\| Northwestern Polytechnical University, China (Yaping Wang)
Abstract	Owning to its clinical accessibility, T1-weighted MRI has been extensively studied for the prediction of mild cognitive impairment (MCI) and Alzheimer's disease (AD). The tissue volumes of GM, WM and CSF are the most commonly used measures for MCI and AD prediction. We note that disease-induced structural changes may not happen at isolated spots, but in several inter-related regions. Therefore, in this paper we propose to directly extract the inter-region connectivity based features for MCI prediction. This involves constructing a brain network for each subject, with each node representing an ROI and each edge representing regional interactions. This network is also built hierarchically to improve the robustness of classification. Compared with conventional methods, our approach produces a significant larger pool of features, which if improperly dealt with, will result in intractability when used for classifier training. Therefore based on the characteristics of the network features, we employ Partial Least Square analysis to efficiently reduce the feature dimensionality to a manageable level while at the same time preserving discriminative information as much as possible. Our experiment demonstrates that without requiring any new information in addition to T1-weighted images, the prediction accuracy of MCI is statistically improved.
Starting Page	1073
Ending Page	1080
File Size	425313
Page Count	8
File Format	PDF
ISBN	9781457703942
ISSN	10636919
e-ISBN	9781457703959
DOI	10.1109/CVPR.2011.5995689
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-06-20
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Correlation Volume measurement Diseases Compounds Brain modeling Vectors
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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