Gene expression based mouse brain parcellation using Markov random field regularized non-negative matrix factorization

Sayan D. Pathak; David R. Haynor; Carol L. Thompson; Ed Lein; Michael Hawrylycz

doi:10.1117/12.813437

27 February 2009 Gene expression based mouse brain parcellation using Markov random field regularized non-negative matrix factorization

Sayan D. Pathak, David R. Haynor, Carol L. Thompson, Ed Lein, Michael Hawrylycz

Author Affiliations +

Proceedings Volume 7262, Medical Imaging 2009: Biomedical Applications in Molecular, Structural, and Functional Imaging; 726212 (2009) https://doi.org/10.1117/12.813437
Event: SPIE Medical Imaging, 2009, Lake Buena Vista (Orlando Area), Florida, United States

Abstract

Understanding the geography of genetic expression in the mouse brain has opened previously unexplored avenues in neuroinformatics. The Allen Brain Atlas (www.brain-map.org) (ABA) provides genome-wide colorimetric in situ hybridization (ISH) gene expression images at high spatial resolution, all mapped to a common three-dimensional 200μm³ spatial framework defined by the Allen Reference Atlas (ARA) and is a unique data set for studying expression based structural and functional organization of the brain. The goal of this study was to facilitate an unbiased data-driven structural partitioning of the major structures in the mouse brain. We have developed an algorithm that uses nonnegative matrix factorization (NMF) to perform parts based analysis of ISH gene expression images. The standard NMF approach and its variants are limited in their ability to flexibly integrate prior knowledge, in the context of spatial data. In this paper, we introduce spatial connectivity as an additional regularization in NMF decomposition via the use of Markov Random Fields (mNMF). The mNMF algorithm alternates neighborhood updates with iterations of the standard NMF algorithm to exploit spatial correlations in the data. We present the algorithm and show the sub-divisions of hippocampus and somatosensory-cortex obtained via this approach. The results are compared with established neuroanatomic knowledge. We also highlight novel gene expression based sub divisions of the hippocampus identified by using the mNMF algorithm.

Citation Download Citation

Sayan D. Pathak, David R. Haynor, Carol L. Thompson, Ed Lein, and Michael Hawrylycz "Gene expression based mouse brain parcellation using Markov random field regularized non-negative matrix factorization", Proc. SPIE 7262, Medical Imaging 2009: Biomedical Applications in Molecular, Structural, and Functional Imaging, 726212 (27 February 2009); https://doi.org/10.1117/12.813437

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KEYWORDS

Brain

Thalamus

Magnetorheological finishing

Computer simulations

Genetic algorithms

Neuroimaging

Somatosensory cortex

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