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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Main Authors:	Yoshida, Hisako, Kawaguchi, Atsushi, Tsuruya, Kazuhiko
Format:	Online
Language:	English
Published:	Hindawi Publishing Corporation 2013
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3666301/

id	pubmed-3666301
recordtype	oai_dc
spelling	pubmed-36663012013-06-12 Radial Basis Function-Sparse Partial Least Squares for Application to Brain Imaging Data Yoshida, Hisako Kawaguchi, Atsushi Tsuruya, Kazuhiko Research Article 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. Hindawi Publishing Corporation 2013 2013-05-13 /pmc/articles/PMC3666301/ /pubmed/23762188 http://dx.doi.org/10.1155/2013/591032 Text en Copyright © 2013 Hisako Yoshida et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Yoshida, Hisako Kawaguchi, Atsushi Tsuruya, Kazuhiko
spellingShingle	Yoshida, Hisako Kawaguchi, Atsushi Tsuruya, Kazuhiko Radial Basis Function-Sparse Partial Least Squares for Application to Brain Imaging Data
author_facet	Yoshida, Hisako Kawaguchi, Atsushi Tsuruya, Kazuhiko
author_sort	Yoshida, Hisako
title	Radial Basis Function-Sparse Partial Least Squares for Application to Brain Imaging Data
title_short	Radial Basis Function-Sparse Partial Least Squares for Application to Brain Imaging Data
title_full	Radial Basis Function-Sparse Partial Least Squares for Application to Brain Imaging Data
title_fullStr	Radial Basis Function-Sparse Partial Least Squares for Application to Brain Imaging Data
title_full_unstemmed	Radial Basis Function-Sparse Partial Least Squares for Application to Brain Imaging Data
title_sort	radial basis function-sparse partial least squares for application to brain imaging data
description	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.
publisher	Hindawi Publishing Corporation
publishDate	2013
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3666301/
_version_	1611981849922371584

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

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