Distributional assumptions in voxel-based morphometry.

@article{Salmond2002DistributionalAI,
  title={Distributional assumptions in voxel-based morphometry.},
  author={Claire Helen Salmond and John Ashburner and Faraneh Vargha-Khadem and Alan Connelly and David G. Gadian and Karl J. Friston},
  journal={NeuroImage},
  year={2002},
  volume={17 2},
  pages={1027-30}
}
In this paper we address the assumptions about the distribution of errors made by voxel-based morphometry. Voxel-based morphometry (VBM) uses the general linear model to construct parametric statistical tests. In order for these statistics to be valid, a small number of assumptions must hold. A key assumption is that the model's error terms are normally distributed. This is usually ensured through the Central Limit Theorem by smoothing the data. However, there is increasing interest in using… CONTINUE READING