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

Conference Paper


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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. © 2011 IEEE.

Authors


  •   Zhou, Luping
  •   Wang, Yaping (external author)
  •   Li, Yang (external author)
  •   Yap, Pew-Thian (external author)
  •   Shen, Dinggang (external author)

Publication Date


  • 2011

Citation


  • Zhou, L., Wang, Y., Li, Y., Yap, P. & Shen, D. (2011). Hierarchical anatomical brain networks for MCI prediction by partial least square analysis. IEEE Conference on Computer Vision and Pattern Recognition (pp. 1073-1080). Providence, United States: IEEE.

Scopus Eid


  • 2-s2.0-80052887241

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=2785&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/1776

Has Global Citation Frequency


Start Page


  • 1073

End Page


  • 1080

Place Of Publication


  • Providence, United States

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. © 2011 IEEE.

Authors


  •   Zhou, Luping
  •   Wang, Yaping (external author)
  •   Li, Yang (external author)
  •   Yap, Pew-Thian (external author)
  •   Shen, Dinggang (external author)

Publication Date


  • 2011

Citation


  • Zhou, L., Wang, Y., Li, Y., Yap, P. & Shen, D. (2011). Hierarchical anatomical brain networks for MCI prediction by partial least square analysis. IEEE Conference on Computer Vision and Pattern Recognition (pp. 1073-1080). Providence, United States: IEEE.

Scopus Eid


  • 2-s2.0-80052887241

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=2785&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/1776

Has Global Citation Frequency


Start Page


  • 1073

End Page


  • 1080

Place Of Publication


  • Providence, United States