Dear Li Bei,
There are clear answers to both these questions. However, they are
more conceptual than practical.
1, Suppose there is an experiment including 2 conditions A, B
and 1 baseline. When contrast A with B using t contrast, the
activation of A may because of the deactivation of B related to
baseline, not the activation of A. In this condition, should I mask t
contrast A>B to eliminate the deactivation of B? I found in most of my
experiment (similar experiment can be easily found in articles
published), baseline > task always got some activations, but almost no
one cared about this in their articles, could you tell me why and how
to deal with this?
1) I do not think there is much to worry about here. One way of
looking at this is to call the baseline condition C. At any voxel,
one of the three conditions (A, B or C) will show the lowest signal.
The only interesting things are the differences among A, B and C. The
only special thing about C is that you assumed, a priori, that C would
have the lowest activity. However, this assumption is not necessarily
correct. There are many instances of event-related reductions in
BOLD and rCBF.
2) If you are only interested in voxels where C has the lowest signal
then (1) define regions that are activated in A and B relative to C,
using a contrast like 1 1 -2. Then, (2) use the ensuing regions to constrain
your search for differences between A and B (e.g., with a contrast -1
1 0). Note you can do this because the contrasts are orthogonal,
This is why we sometimes include null events in event-related designs;
so that we can define a responsive system that constrains the search for
differential responses among the non-null trials.
2, Since the DCM is not exploratory, one must specify a correct
DCM model to get a right answer. But because the rule for building a
DCM model is vague(anatomic connections may not valid under a certain
task, influences can pass through indirect connections between 2 brain
regions), how could one know the model he built is correct? The result
may be quite different because of a tiny modification of the model, I
don't know how much reliance can be put on one model. Could you give
me some suggestions? (the model proposed by andrea in 2004 is a very
complex one, will a small modification change the result of the model??) J
The answer here is yes. Changing the model will change the
conditional inferences (that were conditioned on the original
model). It is surprising or bad? No. The inference is explicitly
conditioned on the model. Can you choose the best model? Yes, one
uses Bayesian modal selection to do this. In short, there are two levels
of inference in DCM. The first is conditional inference about the
coupling parameters of a particular model. The second is inference
made in terms of model selection or comparison, averaging over the
coupling parameters of each model. With these two levels you
can explore a model and the family of models from which your DCM
came. People sometimes think that DCM will find the best model
automatically and tell you how the brain works. It does not. DCM
allows you answer well-formulated and specific questions that are
operationally embodied by a model, or by the differences among
specified models. It does not perform an exploratory search of model
space. If you replace 'DCM' with 'question' then clearly the 'answer'
will change with the 'question'.
PS There is no such thing as a 'correct' DCM. A DCM can only
be more or less likely than another DCM.
Does this help? - Karl
