Radial basis function-sparse partial least squares for application to brain imaging data

Hisako Yoshida, Atsushi Kawaguchi, Kazuhiko Tsuruya

研究成果: ジャーナルへの寄稿学術誌査読

12 被引用数 (Scopus)


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.

ジャーナルComputational and Mathematical Methods in Medicine
出版ステータス出版済み - 2013

!!!All Science Journal Classification (ASJC) codes

  • モデリングとシミュレーション
  • 生化学、遺伝学、分子生物学(全般)
  • 免疫学および微生物学(全般)
  • 応用数学


「Radial basis function-sparse partial least squares for application to brain imaging data」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。