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Proceedings Paper

Sparse brain network using penalized linear regression
Author(s): Hyekyoung Lee; Dong Soo Lee; Hyejin Kang; Boong-Nyun Kim; Moo K. Chung
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Paper Abstract

Sparse partial correlation is a useful connectivity measure for brain networks when it is difficult to compute the exact partial correlation in the small-n large-p setting. In this paper, we formulate the problem of estimating partial correlation as a sparse linear regression with a l1-norm penalty. The method is applied to brain network consisting of parcellated regions of interest (ROIs), which are obtained from FDG-PET images of the autism spectrum disorder (ASD) children and the pediatric control (PedCon) subjects. To validate the results, we check their reproducibilities of the obtained brain networks by the leave-one-out cross validation and compare the clustered structures derived from the brain networks of ASD and PedCon.

Paper Details

Date Published: 9 March 2011
PDF: 6 pages
Proc. SPIE 7965, Medical Imaging 2011: Biomedical Applications in Molecular, Structural, and Functional Imaging, 796517 (9 March 2011); doi: 10.1117/12.877547
Show Author Affiliations
Hyekyoung Lee, Seoul National Univ. College of Medicine (Korea, Republic of)
Seoul National Univ. (Korea, Republic of)
Dong Soo Lee, Seoul National Univ. College of Medicine (Korea, Republic of)
Seoul National Univ. (Korea, Republic of)
Hyejin Kang, Seoul National Univ. College of Medicine (Korea, Republic of)
Seoul National Univ. (Korea, Republic of)
Boong-Nyun Kim, Seoul National Univ. College of Medicine (Korea, Republic of)
Moo K. Chung, Seoul National Univ. (Korea, Republic of)
Univ. of Wisconsin, Madison (United States)


Published in SPIE Proceedings Vol. 7965:
Medical Imaging 2011: Biomedical Applications in Molecular, Structural, and Functional Imaging
John B. Weaver; Robert C. Molthen, Editor(s)

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