Pitfalls in the clustering of neuroimage data and improvements by global optimization strategies.

@article{Mller2001PitfallsIT,
  title={Pitfalls in the clustering of neuroimage data and improvements by global optimization strategies.},
  author={Ulrich M{\"o}ller and Marc Ligges and Carolin Gr{\"u}nling and Petra Georgiewa and Werner A. Kaiser and Herbert Witte and Bernhard Blanz},
  journal={NeuroImage},
  year={2001},
  volume={14 1 Pt 1},
  pages={206-18}
}
In this paper, we examined three vector quantization (VQ) methods used for the unsupervised classification (clustering) of functional magnetic resonance imaging (fMRI) data. Classification means that each brain volume element (voxel), according to a given scanning raster, was assigned to one group of voxels based on similarity of the fMRI signal patterns. It was investigated how the VQ methods can isolate a cluster that describes the region involved in a particular brain function. As an example… CONTINUE READING