Dear Bruno
The problem you encounter may relate to the fact, that only voxels passing an F-threshold of p<0.001 (spm_spm ~ line 450), i.e., those voxels where your model fits reasonably well, enter the non-sphericity estimation (cf. ~ line 700).
If the analysis crashed with an "no inmask voxels", that should be the reason (your noise-model didn't fit anywhere)
If the analysis ran through but you got "no significant voxels" in the inference... this is exactly what happend: Nothing was significant, which is not surprising given you only entered noise.
It's not clear to me, however, why you would want to fit that model anyways, given you know the mean and variance of your data since you actually generated it from a particular generative model.
Best
Simon
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Von: SPM (Statistical Parametric Mapping) [[log in to unmask]]" im Auftrag von "Bruno L. Giordano [[log in to unmask]]
Gesendet: Montag, 14. Mai 2012 17:00
An: [log in to unmask]
Betreff: [SPM] Estimating a second-level model under the null hypothesis
Dear list,
I am trying to fit a null model second level model where the
across-subjects mean of each condition is set to 0. I need the parameter
estimates for resampling under H0 of no between-condition difference.
SPM protests that I have no significant voxel. I would need the
estimates/stats anyways.
What could I do?
Thank you,
Bruno
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