Von: Eric
Zarahn <[log in to unmask]>
An: [log in to unmask]
Gesendet: Sonntag, den
24. Mai 2009, 21:33:13 Uhr
Betreff: Re: [SPM]
DCM: fixed vs. random effects BMS; direction of connectivity change
Dear All,
Is there a most relevant paper for model selection in the context of
DCM as summarized so elegantly in this thread? Thanks in advance.
Eric
Quoting Klaas Enno Stephan <
[log in to unmask]>:
> Dear Darren,
>
> The choice between fixed effects (FFX) and random effects (RFX)
analyses in the context of BMS is no different from choosing between
FFX and RFX in the context of any other statistical analysis (like
SPM): if one believes that the effect of interest (here: model
structure) is a fixed property of the population studied, one should
use a FFX analysis. If, however, if one believes that the effect of
interest is a random variable in the population studied, a RFX
analysis is preferable.
>
> FFX analyses are appropriate, for example, when studying
low-level physiological phenomena where it can (relatively safely) be
assumed that these phenomena exist as fixed properties of the
population and that variability across subjects is due to measurement
noise alone. FFX BMS requires summing of the log evidences across
subjects and then comparing this across models (equivalently:
multiplication of Bayes factors).
>
> RFX analyses should be preferred when studying cognitive
processes (due to potential inter-subject variability in strategy and,
for systems with degeneracy, the possibility that networks are used
differently across subjects to implement task demands) or patients
(due to potential variability in pathophysiology or in the degree in
which brain function has been compromised by the disease). RFX BMS
uses the new Variational Bayes method in SPM8.
>
> Best wishes,
> Klaas
>
>
>
>
>
> ________________________________
> Von: Darren Gitelman <
[log in to unmask]>
> An:
[log in to unmask]
> Gesendet: Freitag, den 22. Mai 2009, 16:40:54 Uhr
> Betreff: Re: [SPM] DCM: fixed vs. random effects BMS; direction
of connectivity change
>
> 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
>>
>
>
>
>
