TY - JOUR

T1 - Finding imaging patterns of structural covariance via Non-Negative Matrix Factorization

AU - Sotiras, Aristeidis

AU - Resnick, Susan M.

AU - Davatzikos, Christos

N1 - Funding Information:
This work was partially supported by the National Institutes of Health (grant numbers R01-MH070365 and R01-AG014971 ) and the Baltimore Longitudinal Study of Aging under contract HHSN271201300284 .
Publisher Copyright:
© 2014 Elsevier Inc.

PY - 2015/3/1

Y1 - 2015/3/1

N2 - In this paper, we investigate the use of Non-Negative Matrix Factorization (NNMF) for the analysis of structural neuroimaging data. The goal is to identify the brain regions that co-vary across individuals in a consistent way, hence potentially being part of underlying brain networks or otherwise influenced by underlying common mechanisms such as genetics and pathologies. NNMF offers a directly data-driven way of extracting relatively localized co-varying structural regions, thereby transcending limitations of Principal Component Analysis (PCA), Independent Component Analysis (ICA) and other related methods that tend to produce dispersed components of positive and negative loadings. In particular, leveraging upon the well known ability of NNMF to produce parts-based representations of image data, we derive decompositions that partition the brain into regions that vary in consistent ways across individuals. Importantly, these decompositions achieve dimensionality reduction via highly interpretable ways and generalize well to new data as shown via split-sample experiments. We empirically validate NNMF in two data sets: i) a Diffusion Tensor (DT) mouse brain development study, and ii) a structural Magnetic Resonance (sMR) study of human brain aging. We demonstrate the ability of NNMF to produce sparse parts-based representations of the data at various resolutions. These representations seem to follow what we know about the underlying functional organization of the brain and also capture some pathological processes. Moreover, we show that these low dimensional representations favorably compare to descriptions obtained with more commonly used matrix factorization methods like PCA and ICA.

AB - In this paper, we investigate the use of Non-Negative Matrix Factorization (NNMF) for the analysis of structural neuroimaging data. The goal is to identify the brain regions that co-vary across individuals in a consistent way, hence potentially being part of underlying brain networks or otherwise influenced by underlying common mechanisms such as genetics and pathologies. NNMF offers a directly data-driven way of extracting relatively localized co-varying structural regions, thereby transcending limitations of Principal Component Analysis (PCA), Independent Component Analysis (ICA) and other related methods that tend to produce dispersed components of positive and negative loadings. In particular, leveraging upon the well known ability of NNMF to produce parts-based representations of image data, we derive decompositions that partition the brain into regions that vary in consistent ways across individuals. Importantly, these decompositions achieve dimensionality reduction via highly interpretable ways and generalize well to new data as shown via split-sample experiments. We empirically validate NNMF in two data sets: i) a Diffusion Tensor (DT) mouse brain development study, and ii) a structural Magnetic Resonance (sMR) study of human brain aging. We demonstrate the ability of NNMF to produce sparse parts-based representations of the data at various resolutions. These representations seem to follow what we know about the underlying functional organization of the brain and also capture some pathological processes. Moreover, we show that these low dimensional representations favorably compare to descriptions obtained with more commonly used matrix factorization methods like PCA and ICA.

KW - Data analysis

KW - Diffusion Tensor Imaging

KW - Fractional anisotropy

KW - Gray matter

KW - Independent Component Analysis

KW - Non-Negative Matrix Factorization

KW - Principal Component Analysis

KW - RAVENS

KW - Structural Magnetic Resonance Imaging

KW - Structural covariance

UR - http://www.scopus.com/inward/record.url?scp=84920160417&partnerID=8YFLogxK

U2 - 10.1016/j.neuroimage.2014.11.045

DO - 10.1016/j.neuroimage.2014.11.045

M3 - Article

C2 - 25497684

AN - SCOPUS:84920160417

VL - 108

SP - 1

EP - 16

JO - NeuroImage

JF - NeuroImage

SN - 1053-8119

ER -