Radial Basis Function-Sparse Partial Least Squares for Application to Brain Imaging Data

Magnetic resonance imaging (MRI) data is an invaluable tool in brain morphology research. Here, we propose a novel statistical method for investigating the relationship between clinical characteristics and brain morphology based on three-dimensional MRI data via radial basis function-sparse partial...

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Bibliographic Details
Published in:Computational and mathematical methods in medicine 2013-01, Vol.2013 (2013), p.1-7
Main Authors: Yoshida, Hisako, Kawaguchi, Atsushi, Tsuruya, Kazuhiko
Format: Article
Language:English
Subjects:
Aged
Brain - anatomy & histology
Brain - pathology
Computational Biology
Computer Simulation
Databases, Factual - statistics & numerical data
Female
Humans
Imaging, Three-Dimensional - statistics & numerical data
Least-Squares Analysis
Magnetic Resonance Imaging - statistics & numerical data
Male
Middle Aged
Models, Neurological
Neuroimaging - statistics & numerical data
Renal Insufficiency, Chronic - pathology
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Summary:Magnetic resonance imaging (MRI) data is an invaluable tool in brain morphology research. Here, we propose a novel statistical method for investigating the relationship between clinical characteristics and brain morphology based on three-dimensional MRI data via radial basis function-sparse partial least squares (RBF-sPLS). Our data consisted of MRI image intensities for multimillion voxels in a 3D array along with 73 clinical variables. This dataset represents a suitable application of RBF-sPLS because of a potential correlation among voxels as well as among clinical characteristics. Additionally, this method can simultaneously select both effective brain regions and clinical characteristics based on sparse modeling. This is in contrast to existing methods, which consider prespecified brain regions because of the computational difficulties involved in processing high-dimensional data. RBF-sPLS employs dimensionality reduction in order to overcome this obstacle. We have applied RBF-sPLS to a real dataset composed of 102 chronic kidney disease patients, while a comparison study used a simulated dataset. RBF-sPLS identified two brain regions of interest from our patient data: the temporal lobe and the occipital lobe, which are associated with aging and anemia, respectively. Our simulation study suggested that such brain regions are extracted with excellent accuracy using our method.
ISSN:1748-670X
1748-6718
DOI:10.1155/2013/591032