Dear Tali,

it is usually not a good idea to use the same 
input as a driving input to one region and as a 
modulator of an afferent connection originating 
in that region.  The reason is that the 
conditional estimates of both parameters will be 
highly correlated and, given that the prior 
variance for driving inputs is higher than for 
modulatory ones, this will tend to over-estimate 
the driving parameters and under-estimate the 
modulatory parameters.  In other words, your 
estimates are based.  (In complex networks this 
is not always true, but it is a good 
rule-of-thumb.  Individual cases are best checked using simulations.)

In your case, it seems best to define an input 
that includes words and symbols (assuming that 
they equally activate the input region, let's 
call it A1) but not fixation (assuming that this 
does not activate A1) and define a second, 
modulatory input that only includes words.  In 
this way, you are testing whether a word-related 
activation in the target region (A2) can be 
explained by a selective increase of the 
connection from A1 to A2 during the presentation of words.

All things above are independent of whether you 
use a blocked or event-related design.

Best wishes,
Klaas

PS. When you say "adjust" to you mean the 
adjustment in the extraction of data (VOI tool)?


At 17:50 14/11/2005, Tali Bitan wrote:
>Dear DCM Experts:
>
>We have a question about how to best disabmiguate
>between driving inputs and bilinear effects using
>a non-factorial, event-related study.
>
>We have 3 conditions in the time series: 'words',
>'symbols' and 'fixation'. We have adjusted the
>ROI to include all 3 effects. We are only
>interested in the effect of words, so in the DCM
>model we only have 'words' as the driving input
>to the input region, and also as exerting a
>bilinear effect on all connections, except for
>the connections that go back to the input region.
>Is this the correct way to go?
>
>Or - do we need to include all 3 effects (words,
>symbols and fixation) as (separate vs. a single
>combined) driving input(s) on the input region,
>while only the 'words' would have a bilinear effect on all connections?
>
>If so - would the situation be different if we
>only adjust the ROI for 'words' (rather than all
>three effects)? Do we still need to include
>'symbols' and 'fixation' as driving inputs?
>
>Is all this true only for an event-related design, or also for a block
>design?
>
>thanks a lot.
>Tali Bitan
>
>---------------------------------------------------------------------------
>----------------------
>Dear Tali here is my first crack at the question:
>
>I've looked through the DCM code. As we thought,
>what it does is it takes the a, b, and c
>parameters and the hemodynamic priors and then
>sets up the expected neuronal and then
>hemodynamic responses. The c parameters determine
>the activity in the system, so no c parameters =
>no system activity. I have tried this and
>verified it. In a sense it almost seems as if DCM
>creates signals in a networks based on the
>driving inputs, and then sees if that signal can
>be alternatively modeled by some combination of
>an interaction and the signals from other
>regions  ((signal x input) + multi-regional
>signal). I think this is why DCM seems best
>conceived when the experiment allows different
>driving and modulatory effects (i.e., based on an ANOVA model)
>
>In usual GLM statistics, if your design matrix
>leaves out (regressors, covariates, conditions)
>that specify important aspects of variance, that
>variance ends up in the residuals and affects the
>p-values. If this variance substantially
>contributes to the residuals and has some defined
>structure it may violate some assumptions of the GLM as well.
>
>I don't know whether the same is true in DCM. If
>you include words as driving inputs and as
>modulatory effects, but the data is based
>on  words+symbols+fixation then 1) the design
>will only generate activity related to words. 2)
>This will potentially work against you when you
>then try to use the same input as a modulatory
>effect. 3) When DCM tries to compare the expected
>signal vs. the actual signal there will be  a significant discrepancy.
>
>What does this all mean about how best to use DCM
>given the difficulty of some experimental
>designs? I'm not sure but I think you should
>probably use words+symbols+fixation as an input
>and just words as your modulatory effect. If you
>just use words as input then the design would
>probably be best for just examining differences
>in the intrinsic models for the network. See
>Ethofer et al., Cerebral pathways in processing
>of affective prosody: A dynamic causal modeling study. Neuroimage, in
>press.
>
>I would be very interested in the thoughts and
>commentary from real DCM experts on your questions and my reply.
>
>Darren
>
> >----------------------------------------------
> >Tali Bitan, PhD
> >Department of Communication Sciences & Disorders
> >Northwestern University, IL
> >Phone (847)467-1549
>
>-------------------------------------------------------------------------
>Darren R. Gitelman, M.D.
>Cognitive Neurology and Alzheimer¹s Disease Center
>Northwestern Univ., 320 E. Superior St., Searle 11-470, Chicago, IL 60611
>Voice:   (312) 908-9023     Fax:  (312) 908-8789
>-------------------------------------------------------------------------