Hi Avi,

Since a constant term is modeled via at least polort 0 in the linear regression, the errts will have mean 0, with or without censoring.

I am not on top of the details of FSL's dual regression, but if it is as you say, then the additional regressor is generated as spatially weighted sum of errts time series. Since each time series has a mean of zero and is also identically zero at the censored time points, the same should hold for that new regressor. So given a spatial weighting volume, including or not including the censored time points should make no difference in correlations. And if that extra regressor is added to the same regression model, those censored time points will still be censored.

One question might arise though from how that new regressor is constructed, how the spatial weighting volume is constructed. It might not matter, but it is conceivable for the zero time points to affect how FSL is generating the weighting volume. Along those lines, it might be good to verify this with the FSL folks.


If you want to be sure, use the $ktrs method from the proc script to:
1. make a list of time points to keep
2. extract those time points from errts to make a shorter errts

See if this behaves differently. You might also need to verify that if the dual regression is run twice (in exactly the same way), the results are the same. If there is randomness in the operation, you will not be able to tell what effect the censored time point have, since the results would change every time anyway.

- rick