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Erschienen in:
2013 | OriginalPaper | Buchkapitel
A New Sparse Simplex Model for Brain Anatomical and Genetic Network Analysis
verfasst von : Heng Huang, Jingwen Yan, Feiping Nie, Jin Huang, Weidong Cai, Andrew J. Saykin, Li Shen
Erschienen in: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013
Verlag: Springer Berlin Heidelberg
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The Allen Brain Atlas (ABA) database provides comprehensive 3D atlas of gene expression in the adult mouse brain for studying the spatial expression patterns in the mammalian central nervous system. It is computationally challenging to construct the accurate anatomical and genetic networks using the ABA 4D data. In this paper, we propose a novel sparse simplex model to accurately construct the brain anatomical and genetic networks, which are important to reveal the brain spatial expression patterns. Our new approach addresses the shift-invariant and parameter tuning problems, which are notorious in the existing network analysis methods, such that the proposed model is more suitable for solving practical biomedical problems. We validate our new model using the 4D ABA data, and the network construction results show the superior performance of the proposed sparse simplex model.