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Hello,
I have some questions relating to the thread below about the
development of DCM over the past several years. I have also
noticed with my data that often models that were inverted
relatively well (at least some of the parameters were
substantially different than their prior expectations) in DCM8
now flatline in DCM 10 (I am not sure if the 'DCM12' Dr. Friston
refers to is just shorthand, but I have the most recent updates
and my DCM still says it is version 10).
I understand that DCM has changed as the priors and inversion
method have evolved, but I wonder whether this corresponds to
any principled reason why DCM should more often conclude that
the data are purely observation noise. For example, has the
variance on the priors changed in a way to make the "shrinkage"
priors more conservative? As I have a strong prior belief that
my data are not purely observation noise (after all, ROIs needed
to pass some activation threshold to be included), is it
appropriate for me to manually adjust the priors on the
precision of the observation noise? If so, could you please
offer some details on how to do this (I can't seem to find this
in the spm_dcm_fmri_priors script)?
I am not sure if this next point is related, but I have been
playing around with stochastic DCMs on the same data (this gives
an inversion that is a little better. It's not a flatline but it
has far fewer significant posterior estimates than with the DCM8
inversion). It gives me some concerning output that I wonder may
indicate some problem with my data:
LAP: 1 (1) F:0.0000e+00 dF:0.00e+00 (3.07e+02
sec)
LAP: 1 (1) F:0.0000e+00 dF:0.00e+00 (3.05e+02
sec)
LAP: 1 (1) F:0.0000e+00 dF:0.00e+00 (3.09e+02
sec)
LAP: 1 (1) F:0.0000e+00 dF:0.00e+00 (3.10e+02
sec)
LAP: 1 (1) F:0.0000e+00 dF:0.00e+00 (3.06e+02
sec)
LAP: 1 (1) F:0.0000e+00 dF:0.00e+00 (3.09e+02
sec)
This inversion may have been unstable;
try reducing DCM.options.s to 1/4
I don't see DCM.options.s anywhere. Is this shorthand for
DCM.options.stochastic?
Thanks for any help or input!
-Ian