Narender
Thanks for this update. I have some questions about it.
On Fri, May 22, 2009 at 3:23 AM, Narender Ramnani <[log in to unmask]> wrote:
> -------------------------------------------
> Dear Narender,
>
>> We are investigating connectivity between two areas using DCM. Our
>> anatomical model simply consisted of
>> forward and backward connections between them.
> ...[Details deleted for brevity]...
>
>> We used random effects Bayesian model selection to distinguish between a
>> number of
>> models (varying the modulating influence of our experimental effect on
>> each connection, and varying the location of the input).
> ...[Details deleted for brevity]...
>
>>
>> (1) Is our model comparison approach appropriate?
>
> Yes - it is compelling and the model space is conceptually nice.
> The only point I would make is that you appear to have used a random-effects
> inference over subjects. This means that a priori, you expect each subject
> could have a different architecture. Usually, in straightforward systems
> neuroscience studies, one assumes that all subjects have the same
> basic architecture (but different parameters). This means the fixed-effect
> pooling of log evidence is more appropriate and usually gives more
> significant results.
If I understand correctly, this suggests it would be reasonable to do
a fixed effects analysis to compare models across subjects, as long as
one thinks that "all subjects have the same basic architecture". This
seems to apply best to groups of normal subjects (or homogeneous
groups). However, in the case of abnormal subjects (e.g.,
neurodegenerative disease) the assumption of having the same basic
architecture might no longer apply, and in that case a random effects
model would more properly apply?
>
>> (2) We are interested in whether or not we can say that modulations
>> represent increased or decreased connectivity. Are we able to make
>> inferences about this from the contents of matrix B (e.g. some values in
>> some subjects are negative)?
>
> Yes, exactly. The best way to report these is to report the % increase or
> decrease in the fixed connectivity (A) implied by the condition specific
> effect (B). This means commuting (100*DCM.Ep.B{i}(j,k)/DCM.EpA(j,k))
> for each connection (j,k) and condition (i). (this is for fMRI, in M/EEG the
> parameters are already gain parameters). For group results you can
> use the group average,where each subject's estimate is weighted
> by its precision
Does this take the place of doing direct t-tests on the B matrix
parameters as has been done in the past? Would it be best to analyze
the "normalized" (B/A) matrix parameters for each condition instead?
Thanks
Darren
> I hope this helps,
>
> Karl
>
>
>
>
>
> --
> Narender Ramnani
> Reader in Cognitive Neuroscience
>
> Cognitive Neuroscience Laboratory
> Department of Psychology
> Royal Holloway University of London
> Egham, Surrey TW20 0EX
>
> Tel: 01784 443519 (Direct)
> Fax: 01784 434347 (Departmental)
> email: [log in to unmask]
>
> www.pc.rhul.ac.uk/staff/n.ramnani
>
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