Dear SPMers,
we would appreciate very much, if someone could help us in setting up an
analysis of effective connectivity (EC). We are new to this kind of
analysis and are encountering some conceptual and practical
difficulties/doubts.

As a preliminar temptative approach we are trying to evaluate a very
simplified model of learning on a set of fmri time series. The model we
are considering is composed by 3 anatomical regions, on the basis of an
a-priori hypothesis: the learning process we are investigating is supposed
to modulate the connections strenghts between the 2 of the 3 areas
considered in the model, due to causal input from the third region. To
test this hypothesis we have been trying to do the following:

1. With SPM99, we first evaluated the t-test contrast comparing condition
A (learning condition) with the baseline B (control). In this way we
indeed, as a-priori postulated, find strong activations in the 3 regions
of interest.

2. From the maximal activated voxel within these 3 regions, we extracted
the first eigenvariate with the V.O.I. function of SPM99.

3. Since our original time series included additional tasks, which we
didn't want to include in the EC analysis, we have edited the first
eigenvariate to consider only those values corresponding to the fmri scans
associated with task A.
We have a first question here: do we have to take into account the delay
of the hemodynamic response when editing the first eigenvariate? In other
words, our TR being 3 sec, if stimulation of task A started at scan n, do
we have to include the values of the first eigenvariate starting from the
value associated with say scan n+2?

4. We then tried with a statistical software package to compute the
covariance matrix between the first eigenvariates of the 3 anatomical
regions considered in the model. However, we are not much confident about
the values found in such a way: our covariance values range from 10E-9 to
10E-8 and this looks a bit suspicious to us (very small covariance?).
A covariance analysis performed in parallel on the values produces values
which are significative. We have found in a few papers, that it might be
possible to include correlation matrices instead of covariance matrices as
an input for EC.
We are confused about the correct approach to follow here.

5. Finally, and what's causing us the most troubles: we have tried to
design our connectivity model with LISREL 8.30 (Student Edition), but we
have repeatedly failed.
We are not sure whether we are doing something wrong and if LISREL is a
suitable software for this type of analysis. Does anyone have any
experience with the Student edition of LISREL or could anybody suggest a
better software?

We would extremely appreciate any suggestion.
Best Regards,

Marco Tettamanti
Istituto Scientifico San Raffaele
Reparto Medicina Nucleare
Via Olgettina 60
I-20132 Milano
Italy
Tel. 0039 - 02 - 26 42 34 60
Fax: 0039 - 02 - 21 71 75 58
email: [log in to unmask]