Next: Signal
Up: Conclusions and Discussion
Previous: Conclusions and Discussion
The noise model consists of a space-time simultaneously specified
autoregressive model. We used a model comparison technique in the
form of DIC, which balanced model complexity with goodness of fit,
to deduce which was the best model out of the ones we considered.
This turned out to be a spatially non-stationary, but temporally
stationary temporal AR(3) model with a within-matter-type
edge-preserving MRF prior on the temporal AR coefficients,
combined with a temporally stationary, but spatially
non-stationary spatial AR(1) model with a within-matter-type
edge-preserving MRF prior on the spatial AR coefficients. We
observed matter-type dependence of the spatial and temporal
autocorrelation of the noise on three different real FMRI
datasets.
It was clear from the DIC model comparisons that use of MRF prior
and ARD prior were both beneficial. However, it is currently not clear how
to implement both within the same model.
Further improvement might include attempting to model the large
scale temporal variations as
in (26,21). In this work we remove the worst of
these by high-pass filtering as a preprocessing step. If this
could be sensibly incorporated into the model then we could be
less conservative with the effective high-pass cut-off point and
the uncertainty associated with estimating these large scale
variations could be taken into account.
Next: Signal
Up: Conclusions and Discussion
Previous: Conclusions and Discussion