Dear Klaas
Following your suggestion, and in order to have non-identical driving
inputs and bilinear effects, we re-analyzed the data (in the conventional
analysis) with the following conditions: 'words' 'symbols' 'fixation'
and 'visual' (the latter includes both 'words' and 'symbols' mixed
together).
The contrast 'words-fixation' now shows different clusters from the 'words-
fixation' in our previous analysis (when 'visual' was not included).
I would like to choose the location of the ROIs for each individual based
on the contrast 'words-fixation' in that previous analysis (in
which 'visual' was not included), since it is not contaminated by the
introduction of the non-orthogonal conditions. If DCM uses the input-
information only to identify the time-points that correspond to 'words'
and does not take into account the variance explained by that input in the
given model, I guess this should be fine.
Is that a reasonable approach?
If not - what do you suggest?
Thanks a lot
Tali
On Tue, 15 Nov 2005 19:15:27 +0000, Klaas Enno Stephan
<[log in to unmask]> wrote:
>Dear Tali,
>
>a quick reply to your questions below:
>
>1. Yes, although to a lesser degree.
>2. The two inputs are not orthogonal, but they
>are not perfectly correlated
>either. Correlations between regressors/inputs
>do not prevent you from estimating the
>parameters. When one regressor/input is a
>linear combination of some others, then some
>contrasts are not estimable but others are - see
>previous entries on the helpline for this general issue.
>3. I would only adjust your extracted data for
>your F "effects of interests" contrast, i.e. simply mean-correct your
data.
>
>Best wishes,
>Klaas
>
>
>At 20:27 14/11/2005, Tali Bitan wrote:
>>Dear Klaas
>>
>>Thanks for your fast response.
>>please see my followup questions below.
>>
>>On Mon, 14 Nov 2005 19:35:23 +0000, Klaas Enno Stephan
>><[log in to unmask]> wrote:
>>
>> >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.
>>
>>1- Would this affect also the modulatory effects on connections that DO
>>NOT involve the input region (i.e. in your example a connection from A2
to
>>A3)?
>>
>>2- If we define an input of both 'words+symbols' (mixed) and another
input
>>of just 'words' in the conventional analysis, they are going to be non-
>>orthogonal, and no variance will be left for the 'words' contrast. Is
this
>>appropriate to do in the conventional model?
>>
>>
>> >
>> >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)?
>>
>>
>>3- yes, by "adjusting the ROI" I refer to the extraction of the data in
>>the VOI tool. If I only adjust for an F contrast of 'words'
>>and 'fixation', does it make any difference? Would I still need to
>>use 'symbols' as a driving input?
>>
>>
>>Thanks a lot.
>>Tali
>>
>>
>> >
>> >
>> >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
>> >>----------------------------------------------------------------------
---
>========================================================================
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