Combined spatial and non-spatial prior for inference on MRI time-series

@article{Groves2009CombinedSA,
  title={Combined spatial and non-spatial prior for inference on MRI time-series},
  author={Adrian R. Groves and Michael A. Chappell and Mark W. Woolrich},
  journal={NeuroImage},
  year={2009},
  volume={45},
  pages={795-809}
}
When modelling FMRI and other MRI time-series data, a Bayesian approach based on adaptive spatial smoothness priors is a compelling alternative to using a standard generalized linear model (GLM) on presmoothed data. Another benefit of the Bayesian approach is that biophysical prior information can be incorporated in a principled manner; however, this requirement for a fixed non-spatial prior on a parameter would normally preclude using spatial regularization on that same parameter. We have… CONTINUE READING

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