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A New Sparse Simplex Model for Brain Anatomical and Genetic Network Analysis
Heng Huang1, Jingwen Yan2, Feiping Nie1, Jin Huang1, Weidong Cai3, Andrew J. Saykin2, and Li Shen2
1Computer Science and Engineering, University of Texas at Arlington, TX, USA
2Radiology and Imaging Sciences, Indiana University School of Medicine, IN, USA
3BMIT Research Group, School of IT, University of Sydney, Australia
Abstract. 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.
LNCS 8150, p. 625 ff.
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© Springer-Verlag Berlin Heidelberg 2013