Good to hear that you are enjoying your new life at Princeton! I hope you have had a good start.
There are two ways in which you could proceed without having to re-estimate your GLMs:
1. For each subject, you could combine the parameter estimates across sessions using the "average" function in DCM. For each parameter estimate, this will give you a weighted mean across sessions (weighted by the session-specific posterior variances). You could then feed the resulting values into a classical second level analysis (e.g. t-test).
2. You can account for the dependency amongst the parameter estimates within each subject by using a repeated measures ANOVA
("repeated measures" because you have multiple parameter estimates for each subject, i.e. one for each session).
----- Ursprüngliche Mail ----
Von: Tobias Overath <
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An: Klaas Stephan <
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Gesendet: Dienstag, den 12. Februar 2008, 17:18:57 Uhr
Betreff: DCM with multiple sessions
Hi
Klaas,
I
hope
you
are
enjoying
your
life
together
in
Zurich
-
I
am
very
happy
here
in
Princeton,
sharing
life
with
Lillian
at
last.
I
have
a
hopefully
quick
question
regarding
multiple
sessions
in
DCM.
I
understand
one
way
is
to
concatenate
the
session
(and
introduce
session
constants
etc.);
however,
I
would
rather
avoid
that
(mainly
for
time
reasons,
i.e.
having
to
re-estimate
the
GLMs).
Instead,
I
have
estimated
each
model
for
each
session
separately
(e.g.
DCM_model1_sess1,
DCM_model1_sess2,
DCM_model1_sess3,
DCM_model1_sess4).
I
have
searched
in
the
SPM
archives
and
in
your
Powerpoint
slides
to
find
a
way
how
to
combine
the
different
session
DCMs
of
the
same
model?
Or
would
you
suggest
I
bite
the
bullet
and
re-estimate
my
GLMs
with
concatenated
sessions?
Best,
Tobias