Dear SPMers,
we have submitted a paper that includes a DCM analysis and have now
received the reviewers` comments. The reviewers ask for modifications of
the DCM analysis. However, we have several doubts concerning the
specific points raised by the reviewers and are unsure how to implement
their requests in practice.

We have a 2x2 factorial design with 4 experimental conditions
(A1,A2,B1,B2) and we are interested in differences in functional
integration between the experimental conditions within a neural system
consisting of one input and several other brain regions. Specifically,
our predictions are that the overall functional integration is greater
for conditions A vs B (main effect) and for (A1-A2)-(B1-B2) (interaction).

The way we have implemented this in a DCM analysis, "inspired" from what
we could gather from previous exchanges on the spm-list (e.g.
http://www.jiscmail.ac.uk/cgi-bin/webadmin?A2=ind06&L=spm&O=D&F=&S=&P=150475),
is the following:

i) The GLM design matrix included, along with the 4 experimental
conditions, a 5th regressors representing all stimuli of all conditions
(i.e. conditions A1, A2, B1, and B2 merged). We called this regressor ALL.

ii) VOIs were extracted from the t-Student main effect (A-B).

iii) ALL was the driving input vector to the input region, whereas the 4
individual experimental conditions were allowed to separately modulate
all the intrinsic connections of the system.

iv) With contrasts on the bilinear parameters of matrix B, we then
assessed whether the individual connections were strengthened
differentially for e.g. (A-B) or for (A1-A2)-(B1-B2).

So much for our implementation. Now the reviewers' points:

One reviewer points out that since the condition ALL is not orthogonal
to the other regressors, this will affect the estimation of the
t-contrast for the extraction of the VOIs (point ii above). The reviewer
suggests to leave out the regressor ALL from the analysis. While we
actually found minimal differences between the results of the GLM
analysis with and without the regressor ALL, we are of course aware that
this is a problem. However, we do not know how we should proceed. Our
understanding is that modelling the 4 experimental conditions as
separate inputs to the DCM model corresponds to asking a different
question, based on the intrinsic connections (matrix A), and not on the
bilinear parameters (matrix B). This is not what we want.
The reviewer suggests that we should instead use the GLM model without
the regressor ALL and "set the contrasts accordingly for the B and C
vectors". We do not know what this means in practice.

Another reviewer, in turn, points out that our "DCM model is
overmodeled" and suggests that we should "re-define the model such that
(1) All [ALL?? (my note)] input is linked to the input region and (2)
only (i) the main effect of A vs B, (ii) the main effect of 1 vs 2 and
(iii) the interaction are entered as modulatory effects. Currently, the
authors allow all four effects to modulate the connection strengths
rendering an interpretation of the intrinsic connectivity and modulatory
effects per se ambiguous."
We have quite a lot of difficulties in understanding how we should
implement the DCM model suggested by this reviewer. How do you enter
e.g. a main effect as a modulatory effect? The DCM model specification
GUI only seems to accept single regressors as modulatory variables and
not e.g. contrasts between regressors.


As a minor point, our fMRI experiment included 9 separate sessions.
Following previous suggestions on the spm-list, we modelled our GLM
design matrix with 1 single super-session including all 9 time series,
and included 9 additional confound regressors modelling the session effects.
One reviewer suggested that we should instead average the results across
sessions or concatenate the time-series of the sessions. The latter
suggestion does not seem viable, since the length of the VOI time series
would not correspond anymore to the size of the SPM.mat design matrix
entered in the DCM model specification GUI.
Are there any strong arguments against modelling "super-sessions" as we did?

One problem related to the "super-session" approach, is that, without
applying Global Scaling to the GLM, the DCM estimation was appearently
soaked up by large spikes within the VOI time series, in coincidence
with the transitions between sessions. In order to remove these spikes,
we applied Global Scaling to the GLM.


Finally (if you were so brave to read down to this point!), one reviewer
argued against our choice of reporting connections at posterior
probabilities of P>0.80, suggesting that we should instead use a more
conventional threshold of P>0.95 or P>0.90.
We understand that a threshold of P>0.80 is quite liberal. On the other
hand, the intrinsic connections were all strongly significant (all
P>0.95). With respect to matrix B, we wished to demonstrate that the
functional integration within the specified neural system was greater
for conditions A vs B. The contrasts on the bilinear terms showed that
in all the connections within the system the direction of the modulatory
effect was always in terms of a greater strength for conditions A than
for conditions B. All the connections showed a P > 0.80, and no
connection showed e.g. a P > 0.10, indicating a stronger connection
strength for B vs A.
The choice of the Bayesian threshold, although quite liberal, was
therefore aimed at showing the overall direction of the modulatory
effects (A>B). Is this fallacious statistical reasoning?


Thank you so much for your patience! I am sorry about the very long
message. On the other hand, I feel that the general SPM users would
benefit a lot from some more detailed practical guidelines illustrating
the use of DCM...

Best,
Marco


Marco Tettamanti, Ph.D.
San Raffaele Scientific Institute
Facoltà di Psicologia
via Olgettina 58
I-20132 Milano (MI)
Italy
Tel. ++39-02-26434888
Fax. ++39-02-26434892
Email: [log in to unmask]