Efficient principal component analysis for multivariate 3D voxel-based mapping of brain functional imaging data sets as applied to FDG-PET and normal aging.

@article{Zuendorf2003EfficientPC,
  title={Efficient principal component analysis for multivariate 3D voxel-based mapping of brain functional imaging data sets as applied to FDG-PET and normal aging.},
  author={Gerhard Zuendorf and Nacer Kerrouche and Karl Herholz and Jean-Claude Baron},
  journal={Human brain mapping},
  year={2003},
  volume={18 1},
  pages={
          13-21
        }
}
Principal component analysis (PCA) is a well-known technique for reduction of dimensionality of functional imaging data. PCA can be looked at as the projection of the original images onto a new orthogonal coordinate system with lower dimensions. The new axes explain the variance in the images in decreasing order of importance, showing correlations between brain regions. We used an efficient, stable and analytical method to work out the PCA of Positron Emission Tomography (PET) images of 74… CONTINUE READING
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