Dear Peter,
I would like to understand something.
I specify several DCM models (on a population of 19 subjects). Then I tested the best model with a Bayesian Model selection RFX inference method.
For data quality check, I have also look at the variance explained by the model using the code 'spm_dcm_fmri_check(DCM)' across my models and subjects.
I am surprise because both methods give opposite result. The best model for BMS method has usually a very low variance explained (below 10%) while several other models have a much better explained variance (above 10%).
Can you explain me why it happen ?
I would like to make comparison between connection weight of the A matrix between experimental conditions. Does it make sense to do it with my best BMS model although the variance explain is very low ?
Thank you very much in advance.
Ps. models which modulates connectivity had usually a better variance explain as modulation on the VOIs according 'spm_dcm_fmri_check(DCM)'. I don't know if this result is related to this fact.
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