1) After your question I have been looking at the VOI data again and I
found that I did
not correct for effects of interest properly when extracting the VOI.
Therefore, I repeated the analysis today using the PPI toolbox of Donald
McLaren
using both the traditional PPI analysis as well as the generalized PPI
analysis.
In both cases I corrected for 'effects of interest' . The traditional
analysis led to exactly
the same result as before, showing decreased correlation in target
trials compared
to standard trials from the seed region to a large network including the
seed region.
The generalized analysis showed no difference at all between target and
standard trials.
I would greatly appreciate any suggestions on what is causing these
effects.
2) The contrasts for the three conditions (standards, targets, novels)
were -1 1 0
(fixation was the baseline and was not explicitly modeled in the design).
Other factors in the design were the movement parameters.
Kind regards,
Linda
Darren Gitelman wrote:
> Linda
>
> 1) Did you adjust the data? (When you click the Eigenvariate button it
> asks if you want to adjust the data.)
>
> 2) You say below that your PPI was [1 -1] for the 2 stimulus types,
> but you list 4 conditions below (standards, targets, novels and
> fixation). What did you include in your PPI and what were the other
> columns of your design?
>
> Darren
>
> On Fri, Jul 1, 2011 at 12:01 PM, L.Geerligs <[log in to unmask]> wrote:
>
>> Thank you for your fast reply.
>> I actually first did the PPI using the standard method in SPM. Then I
>> realized that the PPI and
>> Y terms were correlated and I thought this might cause the results (some of
>> the variance in
>> Y being explained by PPI). Then I tried again with the orthogonalized
>> version of
>> the PPI variable but the results remained the same.
>> The task contains three conditions (standards, targets, novels) and a
>> fixation.
>>
>> Kind regards,
>> Linda
>>
>>
>>
>> On 01-07-11, "MCLAREN, Donald" <[log in to unmask]> wrote:
>>
>> What happens when you use gPPI or PPI in SPM?
>>
>> The way it seems that you did your PPI is non-standard, so its hard to
>> tell if its the method or something about the underlying activity that
>> is making the results look the way they look.
>>
>> Do you have condition A, condition B, and fixation in your task?
>>
>> Best Regards, Donald McLaren
>> =================
>> D.G. McLaren, Ph.D.
>> Postdoctoral Research Fellow, GRECC, Bedford VA
>> Research Fellow, Department of Neurology, Massachusetts General Hospital and
>> Harvard Medical School
>> Office: (773) 406-2464
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>>
>>
>>
>> On Fri, Jul 1, 2011 at 11:06 AM, L. Geerligs <[log in to unmask]> wrote:
>>
>>> Hi SPM users,
>>>
>>> I have a problem with the interpretation of the findings in a
>>> psychophysiological interaction (PPI) analysis.
>>>
>>> Recently I did a PPI analysis on event related fMRI data in which one
>>> event
>>> was presented more frequent than the other (oddball task).
>>> I selected a seed region by using the first eigenvariate of the time
>>> courses
>>> of all voxels in a 6 mm radius around a peak voxel.
>>> Then I looked at the difference between the two stimulus types (contrast 1
>>> -1).
>>> The results of this analysis showed decreased connectivity from the seed
>>> region to a large scale network, in the less frequent condition
>>> compared to the more frequent condition. The network we identified made
>>> sense according to existing literature.
>>> The curious thing about the result is that we also observed a decrease in
>>> connectivity within the brain area which I chose as the seed.
>>> Moreover, when I repeated this analysis with different seed regions, I
>>> found
>>> different networks, but similar decreases of connectivity
>>> with the brain area from which the seed time course was constructed.
>>>
>>> Therefore, I started to wonder about the validity of the findings. Is it
>>> possible that a PPI analysis with conditions with
>>> unequally frequent stimuli gives spurious results? And if not, how can it
>>> be
>>> that I find a decreased regression from the seed region to
>>> itself in one condition compared to another?
>>>
>>> The PPI model was constructed in SPM8, in such a way that the ppi variable
>>> was orthogonal to the Y and P variables (using spm_orth)
>>> and the data was filtered with the first eigenvariate from the signals of
>>> white matter and csf voxels.
>>>
>>> Thanks a lot!
>>>
>>> Kind regards,
>>> Linda
>>>
>>>
>
>
>
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